DeepAIP: Deep learning for anti-inflammatory peptide prediction using pre-trained protein language model features based on contextual self-attention network.

Non-steroidal anti-inflammatory drugs (NSAIDs), glucocorticoids, and other immunosuppressants are commonly used medications for treating inflammation. However, these drugs often come with numerous side effects. Therefore, finding more effective methods for inflammation treatment has become more necessary. The study of anti-inflammatory peptides can effectively address these issues. In this work, we propose a contextual self-attention deep learning model, coupled with features extracted from a pre-trained protein language model, to predict Anti-inflammatory Peptides (AIP). The contextual self-attention module can effectively enhance and learn the features extracted from the pre-trained protein language model, resulting in high accuracy to predict AIP. Additionally, we compared the performance of features extracted from popular pre-trained protein language models available in the market. Finally, Prot-T5 features demonstrated the best comprehensive performance as the input for our deep learning model named DeepAIP. Compared with existing methods on benchmark test dataset, DeepAIP gets higher Matthews Correlation Coefficient and Accuracy score than the second-best method by 16.35 % and 6.91 %, respectively. Performance comparison analysis was conducted using a dataset of 17 novel anti-inflammatory peptide sequences. DeepAIP demonstrates outstanding accuracy, correctly identifying all 17 peptide types as AIP and predicting values closer to the true ones. Data and code are available at https://github.com/YangQingGuoCCZU/DeepAIP.

Brain color-coded diffusion imaging: Utility of ACPC reorientation of gradients in healthy subjects and patients.

BACKGROUND AND OBJECTIVE: The common structural interpretation of diffusion color-encoded (DCE) maps assumes that the brain is aligned with the gradients of the MRI machine. This is seldom achieved in the field, leading to incorrect red (R), green (G) and blue (B) DCE values for the expected orientation of fiber bundles. We studied the virtual reorientation of gradients according to the anterior commissure - posterior commissure (ACPC) system on the RGB derivatives. METHODS: We measured mean ± standard deviation of average, standard deviation, skewness and kurtosis of RGB derivatives, before (rO) and after (acpcO) gradient reorientation, in one healthy-subject group with head routinely positioned (HS-routine), and in two patient groups, one with essential tremor (ET-Opti), and one with Parkinson's disease (PD-Opti), with head position optimized according to ACPC before acquisition. We studied the pitch, roll and yaw angles of reorientation, and we compared rO and acpcO conditions, and groups (ad hoc statistics). RESULTS: Pitch (maximum in the HS-routine group) was greater than roll and yaw. After reorientation of gradients, in the HS-routine group, DCE average increased, and Stddev, skewness and kurtosis decreased; R, G and B average increased, and R and B skewness and kurtosis decreased. By contrast, in the ET-Opti group and the PD-Opti group, R, G and B, average and Stddev increased, and skewness and kurtosis decreased. In both rO and acpcO conditions, in the ET-Opti and PD-Opti groups, average and standard deviation were higher, while skewness and kurtosis were lower. CONCLUSIONS: DCE map interpretability depends on brain orientation. Reorientation realigns gradients with the anatomic and physiologic position of the head and brain, as exemplified.

Contrasting Deficits in the Function of the Phonological Loop Between Children With Attention Deficit Hyperactivity Disorder and Reading Disability.

Attention Deficit Hyperactivity Disorder (ADHD) and reading disability (RD) often co-occur. Impairments in the phonological loop of working memory are central to RD, but it is unclear whether this is linked to ADHD, especially in Chinese reading. Two experiments were conducted with Chinese children aged 7 to 12 (Experiment 1, n = 65; Experiment 2, n = 60). Immediate and delayed recognition paradigms were used to assess phonological encoding and rehearsal. Both the RD-only and comorbid (ADHD + RD) groups performed worse than the control and ADHD-only groups in response time and accuracy. Notably, the comorbid group performed similarly to the RD-only group, indicating that phonological loop deficits in comorbid children are likely due to RD, not ADHD. This highlights phonological loop function as the key to distinguishing between ADHD and RD.

Parietal memory network and memory encoding versus retrieval impairments in PD-MCI patients: A hippocampal volume and cortical thickness study.

OBJECTIVE: The pathophysiology behind memory impairment in Parkinson's Disease Mild Cognitive Impairment (PD-MCI) is unclear. This study aims to investigate the hippocampal and cortical atrophy patterns in PD-MCI patients with different types of memory impairments, categorized as Retrieval Failure (RF) and Encoding Failure (EF). METHODS: The study included 16 healthy controls (HC) and 34 PD-MCI patients, divided into RF (N = 18) and EF (N = 16) groups based on their Verbal Memory Processes Test (VMPT) scores, including spontaneous recall, recognition, and Index of Sensitivity to Cueing (ISC). Hippocampal subfields and cortical thicknesses were measured using the FreeSurfer software for automatic segmentation. RESULTS: Compared to the HC group, the EF group exhibited significant atrophy in the left lateral occipital region and the right caudal middle frontal, superior temporal, and inferior temporal regions (p⟨0.05). The RF group displayed significant atrophy in the left lateral occipital, middle temporal, and precentral regions, as well as the right pars orbitalis and superior frontal regions (p⟨0.05). Hippocampal subfield analysis revealed distinct volume differences between HC-EF and RF-EF groups, with significant reductions in the CA1, CA3, and CA4 subregions in the EF group, but no differences between HC and RF groups (p > 0.05). CONCLUSION: Gray matter atrophy patterns differ in PD-MCI patients with encoding and retrieval memory impairments. The significant hippocampal atrophy in the EF group, particularly in the CA subregions, highlights its potential role in disease progression and memory decline. Additionally, the convergence of atrophy in the lateral occipital cortex across both RF and EF groups suggests the involvement of the Parietal Memory Network (PMN) in PD-related memory impairment.

Neural circuits for goal-directed navigation across species.

Across species, navigation is crucial for finding both resources and shelter. In vertebrates, the hippocampus supports memory-guided goal-directed navigation, whereas in arthropods the central complex supports similar functions. A growing literature is revealing similarities and differences in the organization and function of these brain regions. We review current knowledge about how each structure supports goal-directed navigation by building internal representations of the position or orientation of an animal in space, and of the location or direction of potential goals. We describe input pathways to each structure - medial and lateral entorhinal cortex in vertebrates, and columnar and tangential neurons in insects - that primarily encode spatial and non-spatial information, respectively. Finally, we highlight similarities and differences in spatial encoding across clades and suggest experimental approaches to compare coding principles and behavioral capabilities across species. Such a comparative approach can provide new insights into the neural basis of spatial navigation and neural computation.

Expressibility-induced Concentration of Quantum Neural Tangent Kernels.

Quantum tangent kernel methods provide an efficient approach to analyzing the performance of quantum machine learning models in the infinite-width limit, which is of crucial importance in designing appropriate circuit architectures for certain learning tasks. Recently, they have been adapted to describe the convergence rate of training errors in quantum neural networks in an analytical manner. Here, we study the connections between the expressibility and value concentration of quantum tangent kernel models. In particular, for global loss functions, we rigorously prove that high expressibility of both the global and local quantum encodings can lead to exponential concentration of quantum tangent kernel values to zero. Whereas for local loss functions, such issue of exponential concentration persists owing to the high expressibility, but can be partially mitigated. We further carry out extensive numerical simulations to support our analytical theories. Our discoveries unveil a fundamental feature of quantum neural tangent kernels, indicating that the issue of their concentration cannot be bypassed merely by transitioning to a local encoding scheme while maintaining high expressibility. This offers valuable insights for the design of wide quantum variational circuit models in practical applications.

PAGER: A novel genotype encoding strategy for modeling deviations from additivity in complex trait association studies.

BACKGROUND: The additive model of inheritance assumes that heterozygotes (Aa) are exactly intermediate in respect to homozygotes (AA and aa). While this model is commonly used in single-locus genetic association studies, significant deviations from additivity are well-documented and contribute to phenotypic variance across many traits and systems. This assumption can introduce type I and type II errors by overestimating or underestimating the effects of variants that deviate from additivity. Alternative genotype encoding strategies have been explored to account for different inheritance patterns, but they often incur significant computational or methodological costs. To address these challenges, we introduce PAGER (Phenotype Adjusted Genotype Encoding and Ranking), an efficient pre-processing method that encodes each genetic variant based on normalized mean phenotypic differences between diallelic genotype classes (AA, Aa, and aa). This approach more accurately reflects each variant's true inheritance model, improving model precision while minimizing the costs associated with alternative encoding strategies. RESULTS: Through extensive benchmarking on SNPs simulated with both binary and continuous phenotypes, we demonstrate that PAGER accurately represents various inheritance patterns (including additive, dominant, recessive, and heterosis), achieves levels of statistical power that meet or exceed other encoding strategies, and attains computation speeds up to 55 times faster than a similar method, EDGE. We also apply PAGER to publicly available real-world data and identify a novel, relevant putative QTL associated with body mass index in rats (Rattus norvegicus) that is not detected with the additive model. CONCLUSIONS: Overall, we show that PAGER is an efficient genotype encoding approach that can uncover sources of missing heritability and reveal novel insights in the study of complex traits while incurring minimal costs.

Melatonin ameliorates chronic sleep deprivation against memory encoding vulnerability: involvement of synapse regulation via the mitochondrial-dependent redox homeostasis-induced autophagy inhibition.

Voluntary sleep curtailment is increasingly more rampant in modern society and compromises healthy cognition, including memory, to varying degrees. However, whether memory encoding is impaired after chronic sleep deprivation (CSD) and the underlying molecular mechanisms involved remain unclear. Here, using the mice, we tested the impact of CSD on the encoding abilities of social recognition-dependent memory and object recognition-dependent memory. We found that memory encoding was indeed vulnerable to CSD, while memory retrieval remained unaffected. The hippocampal neurons of mice with memory encoding deficits exhibited significant synapse damage and hyperactive autophagy, which dissipates during regular sleep cycles. This excessive autophagy appeared to be triggered by damage to mitochondrial DNA (mtDNA), resulting from oxidative stress within the mitochondria. The relief at the behavioral and molecular biological levels can be achieved with intraperitoneal injections of the antioxidant compound melatonin. Moreover, our in vitro experiments using HT-22 cells demonstrated that oxidative stress induced by hydrogen peroxide led to oxidative damage, including mtDNA damage, and activation of autophagy. Melatonin treatment effectively countered these effects, restoring redox homeostasis and reducing excessive autophagic activity. Notably, this protective effect was not observed when melatonin was administered as a pre-treatment. Together, our findings reveal the vulnerability of memory encoding during chronic sleep curtailment, which is caused by oxidative stress and consequent enhancement of autophagy, suggest a potential therapeutic strategy for addressing these effects following prolonged wakefulness through melatonin intervention, and reiterate the significance of adequate sleep for memory formation and retention.

Imaging evaluation focused on microstructural tissue changes using tensor-valued diffusion encoding in breast cancers after neoadjuvant chemotherapy: is it a promising way forward?

Background: Single diffusion encoding is a widely used, noninvasive technique for probing the tissue microstructure in breast tumors. However, it does not provide detailed information about the microenvironmental complexity. This study investigated the clinical utility of tensor-valued diffusion encoding for evaluating microstructural changes in breast cancer after neoadjuvant chemotherapy (NAC). Methods: We retrospectively included patients underwent chemotherapy for histologically proven invasive breast cancer between July 2020 and June 2023 and monitored the tumor response with breast magnetic resonance imaging (MRI), including tensor-valued diffusion encoding. We reviewed pre- and post-NAC MRIs regarding chemotherapy in 23 breast cancers. Q-space trajectory imaging (QTI) parameters were estimated at each time-point, and were compared with histopathological parameters. Results: The mean total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), and microscopic fractional anisotropy (µFA) were significantly decreased on post-NAC MRI compared with pre-NAC MRI, with the large effect size (ES) in MKA and µFA (0.81±0.41 vs. 0.99±0.33, ES: 0.48, P=0.03; 0.48±0.30 vs. 0.73±0.27, ES: 0.88, P<0.001; 0.58±0.14 vs. 0.68±0.11, ES: 0.79, P=0.003; respectively). Regarding prognostic factors, tumors with high Ki-67 expression showed significantly lower pre-NAC mean diffusivity (MD) and higher pre-NAC µFA compared to tumors with low Ki-67 expression (0.98±0.09 vs. 1.25±0.20, P=0.002; and 0.72±0.07 vs. 0.57±0.10, P=0.005; respectively). And negative progesterone receptor (PR) group revealed significantly lower MKT, MKA, and isotropic mean kurtosis than positive PR group on the post-NAC MRI (0.60±0.31 vs. 1.03±0.40, P=0.008; 0.36±0.21 vs. 0.61±0.33, P=0.04; and 0.23±0.17 vs. 0.42±0.25, P=0.046; respectively). Conclusions: QTI parameters reflected the microstructural changes in breast cancer treated with NAC and can be used as noninvasive imaging biomarkers correlated with prognostic factors.

Effectiveness of production and drawing as encoding techniques on recall using mixed- and pure-list designs.

We compared the benefit of production and drawing on recall of concrete and abstract words, using mixed- and pure-list designs. We varied stimulus and list types to examine whether the memory benefit from these strategies was sustained across these manipulations. For all experiments, the memory retrieval task was free recall. In Experiment 1, participants studied concrete and abstract words sequentially, with prompts to either silently-read, read aloud, write, or draw each target (intermixed). Reading aloud, writing, and drawing improved recall compared to silent reading, with drawing leading to the largest boost. Performance, however, was at floor in all but the drawing condition. In Experiment 2, the number of targets was reduced, and each strategy (between-subjects) was compared to silent-reading. We eliminated floor effects and replicated results from Experiment 1. In Experiment 3, we manipulated strategy in a pure-list-design. The drawing benefit was maintained while that from production was eliminated. In all experiments, recall was higher for concrete than abstract words that were drawn; no such effect was found for words produced. Results suggest that drawing facilitates memory by enhancing semantic elaboration, whereas the production benefit is largely perceptually based. Importantly, the memory benefit conferred by drawing at encoding, unlike production, cannot be explained by a distinctiveness account as it was relatively unaffected by study design.

Which Encoding Techniques Facilitate Comprehension?

Previous work suggests that similar cognitive processes contribute to memory and comprehension. This is unsurprising as both begin with a common process: encoding. Despite this, the investigation of techniques that benefit memory and comprehension has proceeded separately. In the current study, we compared the robust memory techniques of production and drawing to a similarly effective comprehension strategy known as paraphrasing. Depending on the group, participants were asked to either engage in one of the encoding types (read aloud, draw, or paraphrase) or to silently read 20 term-definition pairs (randomly intermixed and counterbalanced). The encoding techniques of drawing and paraphrasing resulted in better performance on a multiple-choice test of concept comprehension, relative to silently reading. By contrast, reading aloud at encoding did not lead to any benefit relative to silently reading. The results suggest that techniques that invoke transformation of the to-be-remembered text into another format, be it into a picture (drawing) or personally relevant summary (paraphrasing), are particularly effective at improving comprehension. By contrast, encoding techniques that mainly provide a perceptual repetition (production and silent reading) are less effective.

Negative self-referent cognition predicts future depression symptom change: an intensive sampling approach.

Cognitive theories of depression assert that negative self-referent cognition has a causal role in the development and maintenance of depression symptoms, but few studies have examined temporal associations between these constructs using intensive, longitudinal sampling strategies. In three samples of undergraduate students, we examined associations between change in self-referent processing and depression across 5 daily assessments (Sample 1, N = 303, 1,194 measurements, 79% adherence), 7 daily assessments (Sample 2, N = 313, 1,784 measurements, 81% adherence), and 7 weekly assessments (Sample 3; N = 155, 833 measurements, 81% adherence). Random intercept cross-lagged panel models indicated large cross-lagged effects in two of the three samples (Samples 1 and 3 but not Sample 2), such that more negative self-referent thinking than usual was significantly associated with a subsequent increase in depression symptoms at the next time lag. Notably, change in depression from usual was not associated with increases in negative self-referent processing at the next time point in any sample. These findings suggest that change in negative self-referent processing may be causally linked to future increases in depression on a day-to-day and week-to-week basis, although confidence in this conclusion is tempered somewhat by a lack of replication in Sample 2.

The Potential of Phase Constraints for Non-Fourier Radiofrequency-Encoded MRI.

In modern magnetic resonance imaging, it is common to use phase constraints to reduce sampling requirements along Fourier-encoded spatial dimensions. In this work, we investigate whether phase constraints might also be beneficial to reduce sampling requirements along spatial dimensions that are measured using non-Fourier encoding techniques, with direct relevance to approaches that use tailored spatially-selective radiofrequency (RF) pulses to perform spatial encoding along the slice dimension in a 3D imaging experiment. In the first part of the paper, we use the Cramér-Rao lower bound to examine the potential estimation theoretic benefits of using phase constraints. The results suggest that phase constraints can be used to improve experimental efficiency and enable acceleration, but only if the RF encoding matrix is complex-valued and appropriately designed. In the second part of the paper, we use simulations of RF-encoded data to test the benefits of phase constraints combined with optimized RF-encodings, and find that the theoretical benefits are indeed borne out empirically. These results provide new insights into the potential benefits of phase constraints for RF-encoded data, and provide a solid theoretical foundation for future practical explorations.

PELMI: Realize robust DNA image storage under general errors via parity encoding and local mean iteration.

DNA molecules as storage media are characterized by high encoding density and low energy consumption, making DNA storage a highly promising storage method. However, DNA storage has shortcomings, especially when storing multimedia data, wherein image reconstruction fails when address errors occur, resulting in complete data loss. Therefore, we propose a parity encoding and local mean iteration (PELMI) scheme to achieve robust DNA storage of images. The proposed parity encoding scheme satisfies the common biochemical constraints of DNA sequences and the undesired motif content. It addresses varying pixel weights at different positions for binary data, thus optimizing the utilization of Reed-Solomon error correction. Then, through lost and erroneous sequences, data supplementation and local mean iteration are employed to enhance the robustness. The encoding results show that the undesired motif content is reduced by 23%-50% compared with the representative schemes, which improves the sequence stability. PELMI achieves image reconstruction under general errors (insertion, deletion, substitution) and enhances the DNA sequences quality. Especially under 1% error, compared with other advanced encoding schemes, the peak signal-to-noise ratio and the multiscale structure similarity address metric were increased by 10%-13% and 46.8%-122%, respectively, and the mean squared error decreased by 113%-127%. This demonstrates that the reconstructed images had better clarity, fidelity, and similarity in structure, texture, and detail. In summary, PELMI ensures robustness and stability of image storage in DNA and achieves relatively high-quality image reconstruction under general errors.

Accelerated Chemical Shift Encoded Cardiac MRI with Use of Resolution Enhancement Network.

BACKGROUND: Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning network (FastCSE) to accelerate CSE. METHODS: FastCSE was built on a super-resolution generative adversarial network extended to enhance complex-valued image sharpness. FastCSE enhances each echo image independently before water-fat separation. FastCSE was trained with retrospectively identified cines from 1519 patients (56 ± 16 years; 866 men) referred for clinical 3T CMR. In a prospective study of 16 participants (58 ± 19 years; 7 females) and 5 healthy individuals (32 ± 17 years; 5 females), dual-echo CSE images were collected with 1.5 × 1.5mm2, 2.5 × 1.5 mm2, and 3.8 × 1.9mm2 resolution using generalized autocalibrating partially parallel acquisition (GRAPPA). FastCSE was applied to images collected with resolution of 2.5 × 1.5mm2 and 3.8 × 1.9 mm2 to restore sharpness. Fat images obtained from two-point Dixon reconstruction were evaluated using a quantitative blur metric and analyzed with 5-way analysis of variance. RESULTS: FastCSE successfully reconstructed CSE images inline. FastCSE acquisition, with a resolution of 2.5 × 1.5mm² and 3.8 × 1.9 mm², reduced the number of breath-holds without impacting visualization of fat by approximately 1.5-fold and 3-fold compared to GRAPPA acquisition with a resolution of 1.5 × 1.5 mm², from 3.0 ± 0.8 breath-holds to 2.0 ± 0.2 and 1.1 ± 0.4 breath-holds, respectively. FastCSE improved image sharpness and removed ringing artifacts in GRAPPA fat images acquired with a resolution of 2.5 × 1.5 mm2 (0.31 ± 0.03 vs. 0.35 ± 0.04, P < 0.001) and 3.8 × 1.9 mm2 (0.31 ± 0.03 vs. 0.42 ± 0.06, P < 0.001). Blurring in FastCSE images was similar to blurring in images with 1.5 × 1.5 mm² resolution (0.32 ±0.03 vs. 0.31 ± 0.03, P = 0.78; 0.32 ± 0.03 vs. 0.31 ± 0.03, P = 0.90). CONCLUSION: We showed that a deep learning-accelerated CSE technique based on complex-valued resolution enhancement can reduce the number of breath-holds in CSE imaging without impacting the visualization of fat. FastCSE showed similar image sharpness compared to a standardized parallel imaging method.

Massive Electro-Microfluidic Particle Assembly Patterns in Droplet Array for Information Encoding.

The assembly of colloidal particles into micro-patterns is essential in optics, informatics, and microelectronics. However, it is still a challenge to achieve quick, reversible, and precise assembly patterns within micro-scale spaces like droplets. Hereby, a method is presented that utilizes in-plane dielectrophoresis to precisely manipulate particle assemblies within microscale droplets. The electro-microfluidic particle assembly platform, equipped with ingenious electrode designs, enables the formation of diverse micro-patterns within a droplet array. The tunability, similarity, stability, and reversibility of this platform are demonstrated. The ability to assemble letters, numbers, and Morse code patterns within the droplet array underscores its potential for information encoding. Furthermore, using an example with four addressing electrodes beneath a droplet, 16 distinct pieces of information through electrical stimuli is successfully encoded. This unique capability facilitates the construction of a dynamic electronic token, indicating promising applications in anti-counterfeiting technologies.

The role of facial distinctiveness in the prioritisation of targets in disjunctive dual-target face search.

Two experiments explored the search for pairs of faces in a disjunctive dual-target face search (DDTFS) task for unfamiliar face targets. The distinctiveness of the target was manipulated such that both faces were typical or distinctive or contained one typical and one distinctive target. Targets were searched for in arrays of eight faces. In Experiment 1, participants completed a DDTFS block with targets learnt over the block of trials. In Experiment 2, the dual-target block was preceded by two training blocks of single-target trials. Participants also completed the upright and inverted long-form Cambridge Face Memory Test (CFMT+). The results showed that searching for two typical faces leads to one target being prioritised at the expense of the other. The ability to search for non-prioritised typical faces was associated with scores on the CFMT+. This association disappeared when faces were learnt before completing DDTFS. We interpret the findings in terms of the impact of typicality on face learning, individual differences in the ability to learn faces, and the involvement of capacity-limited working memory in the search for unfamiliar faces. The findings have implications for security-related situations where agents must search for multiple unfamiliar faces having been shown their images.

Security officers (e.g. police officers) are often required to be on the lookout for specific individuals or suspects. The present study shows that there is a profound challenge in finding unfamiliar targets when searching for more than one face at the same time. Importantly, the nature of this challenge depends on two factors: first, the relative typicality of the faces that are being sought at the same time, and second, the face processing ability of the searchers. The findings have implications for the design of the job roles and the recruitment of security officers tasked with searching for specific individuals.

A novel DRL-guided sparse voxel decoding model for reconstructing perceived images from brain activity.

BACKGROUND: Due to the sparse encoding character of the human visual cortex and the scarcity of paired training samples for {images, fMRIs}, voxel selection is an effective means of reconstructing perceived images from fMRI. However, the existing data-driven voxel selection methods have not achieved satisfactory results. NEW METHOD: Here, a novel deep reinforcement learning-guided sparse voxel (DRL-SV) decoding model is proposed to reconstruct perceived images from fMRI. We innovatively describe voxel selection as a Markov decision process (MDP), training agents to select voxels that are highly involved in specific visual encoding. RESULTS: Experimental results on two public datasets verify the effectiveness of the proposed DRL-SV, which can accurately select voxels highly involved in neural encoding, thereby improving the quality of visual image reconstruction. COMPARISON WITH EXISTING METHODS: We qualitatively and quantitatively compared our results with the state-of-the-art (SOTA) methods, getting better reconstruction results. We compared the proposed DRL-SV with traditional data-driven baseline methods, obtaining sparser voxel selection results, but better reconstruction performance. CONCLUSIONS: DRL-SV can accurately select voxels involved in visual encoding on few-shot, compared to data-driven voxel selection methods. The proposed decoding model provides a new avenue to improving the image reconstruction quality of the primary visual cortex.

Spiking neural networks for physiological and speech signals: a review.

The integration of Spiking Neural Networks (SNNs) into the analysis and interpretation of physiological and speech signals has emerged as a groundbreaking approach, offering enhanced performance and deeper insights into the underlying biological processes. This review aims to summarize key advances, methodologies, and applications of SNNs within these domains, highlighting their unique ability to mimic the temporal dynamics and efficiency of the human brain. We dive into the core principles of SNNs, their neurobiological underpinnings, and the computational advantages they bring to signal processing, particularly in handling the temporal and spatial complexities inherent in physiological and speech data. Comparative analyses with conventional neural network models are presented to underscore the superior efficiency, lower power consumption, and higher temporal resolution of SNNs. The review further explores challenges and future prospects, highlighting the potential of SNNs to revolutionize wearable healthcare monitoring systems, neuroprosthetic devices, and natural language processing technologies. By providing a comprehensive overview of current strategies, this review aims to inspire innovative approaches in the field, fostering advances in real-time and energy-efficient processing of complex biological signals.

Age-related decline in source and associative memory.

This review explores the multifaceted nature of age-related decline in source memory and associative memory. The review highlights the potential effects of age-related decline in these types of memory. By integrating insights from behavioral, cognitive, and neuroscientific research, it examines how encoding, retrieval, and neural mechanisms influence this decline. Understanding these processes is critical to alleviate memory decline in older adults. Directing attention to source information during encoding, employing unitization techniques to strengthen memory associations, and utilizing metacognitive strategies to focus on relevant details show promise in enhancing memory retrieval for older adults. However, the review acknowledges limitations in processing resources and executive function, necessitating a nuanced approach to the complexities of age-related decline. In conclusion, this review underscores the importance of understanding the complexities of age-related source and associative memory decline and the potential benefits of specific cognitive strategies. It emphasizes the need for continued research on age-related memory function to improve the quality of life for aging populations.