Independent Memory Research Articles

Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data

Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning and operation of existing power systems. To enhance the accuracy of PV power prediction and ensure the safe operation of the power system, a novel approach based on seasonal division and a periodic attention mechanism (PAM) for PV power prediction is proposed. First, the dataset is divided into three components of trend, period, and residual under fuzzy c-means clustering (FCM) and the seasonal decomposition (SD) method according to four seasons. Three independent bidirectional long short-term memory (BiLTSM) networks are constructed for these subsequences. Then, the network is optimized using the improved Newton–Raphson genetic algorithm (NRGA), and the innovative PAM is added to focus on the periodic characteristics of the data. Finally, the results of each component are summarized to obtain the final prediction results. A case study of the Australian DKASC Alice Spring PV power plant dataset demonstrates the performance of the proposed approach. Compared with other paper models, the MAE, RMSE, and MAPE performance evaluation indexes show that the proposed approach has excellent performance in predicting output power accuracy and stability.

STORMM: Structure and topology replica molecular mechanics for chemical simulations.

The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, vectorized central processor units and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM is built to run thousands of independent molecular mechanical calculations on a single GPU with novel implementations that tune numerical precision, mathematical operations, and scarce on-chip memory resources to optimize throughput. The libraries are built around accessible classes with detailed documentation, supporting fine-grained parallelism and algorithm development as well as copying or swapping groups of systems on and off of the GPU. A primary intention of the STORMM libraries is to provide developers of atomic simulation methods with access to a high-performance molecular mechanics engine with extensive facilities to prototype and develop bespoke tools aimed toward drug discovery applications. In its present state, STORMM delivers molecular dynamics simulations of small molecules and small proteins in implicit solvent with tens to hundreds of times the throughput of conventional codes. The engineering paradigm transforms two of the most memory bandwidth-intensive aspects of condensed-phase dynamics, particle-mesh mapping, and valence interactions, into compute-bound problems for several times the scalability of existing programs. Numerical methods for compressing and streamlining the information present in stored coordinates and lookup tables are also presented, delivering improved accuracy over methods implemented in other molecular dynamics engines. The open-source code is released under the MIT license.

Mother–child memory conversation and children’s independent memory: the roles of maternal characteristics

ABSTRACT Mother–child memory conversations are a nuanced and important factor in children’s memory development. The current study focuses on maternal characteristics that are related to individual differences in maternal elaborative style. It also examines the role of maternal elaborative style in children's elaborativeness in the context of reminiscing and recounting. Two hundred and nine Turkish mothers (Mage = 36.32, SD = 4.99) and their 5- to 6-year-olds (Mage in months = 66.88, SD = 4.04) (110 girls, 99 boys) participated in the current study. Results revealed that maternal individuation and balanced self-construal type predicted maternal elaborativeness, which in turn predicted child elaborativeness in reminiscing and recounting. Yet, such a relation was not observed for maternal attachment styles or sensitivity. Findings suggested the importance of maternal individuation and balanced self-construal for mothers’ and children’s elaborativeness in memory conversations.

T cell independent antibody responses with class switch and memory using peptides anchored on liposomes

Vaccines generally require T lymphocytes for B-cell activation and immunoglobulin class switching in response to peptide or protein antigens. In the absence of T cells, limited IgG class switch takes place, germinal centers are short-lived, and the B cells lack memory. Here, immunization of mice with liposomes containing 15mer peptides and monophosphoryl lipid A (MPLA) as adjuvant, induced T-cell independent (TI) IgG class switch within three days, as well as germinal center formation. The antibody responses were long-lived, strictly dependent on Toll-like receptor 4 (TLR4) signaling, partly dependent on Bruton’s tyrosine kinase (BTK) signal transmission, and independent of signaling through T-cell receptors, MHC class II and inflammasome. The antibody response showed characteristics of both TI type 1 and TI type 2. All IgG subclasses could be boosted months after primary immunization, and the biological function of the secreted antibodies was demonstrated in murine models of allergic anaphylaxis and of bacterial infection. Moreover, antibody responses after immunization with peptide- and MPLA-loaded liposomes could be triggered in neonatal mice and in mice receiving immune-suppressants. This study demonstrates T-cell independent endogenous B-cell memory and recall responses in vivo using a peptide antigen. The stimulation of these antibody responses required a correct and dense assembly and administration of peptide and adjuvant on the surface of liposomes. In the future, TI vaccines may prove beneficial in pathological conditions in which T-cell immunity is compromised through disease or medicines or when rapid, antibody-mediated immune protection is needed.

Creation of memory-memory entanglement in a metropolitan quantum network.

Towards realizing the future quantum internet1,2, a pivotal milestone entails the transition from two-node proof-of-principle experiments conducted in laboratories to comprehensive multi-node set-ups on large scales. Here we report the creation of memory-memory entanglement in a multi-node quantum network over a metropolitan area. We use three independent memory nodes, each of which is equipped with an atomic ensemble quantum memory3 that has telecom conversion, together with a photonic server where detection of a single photon heralds the success of entanglement generation. The memory nodes are maximally separated apart for 12.5 kilometres. We actively stabilize the phase variance owing to fibre links and control lasers. We demonstrate concurrent entanglement generation between any two memory nodes. The memory lifetime is longer than the round-trip communication time. Our work provides a metropolitan-scale testbed for the evaluation and exploration of multi-node quantum network protocols and starts a stage of quantum internet research.

Contributions of Learning Attitude, Learning Motivation, Learning Strategy, Memory and Metacognitive toward Upper Elementary Schools Students’ Learning Achievements in Banten Province Indonesia

The aim of this research is to find out how learning attitudes, learning motivation, learning strategy, memory and metacognition contribute to upper students’ learning achievement of State Elementary Schools in Banten Province. This research uses a quantitative approach to ex post facto correlation. In this research, questionnaires and documentation were used to collect data. Correlation test using multiple linear regression was used to analyze the data. Based on the results of the research and data analysis that has been carried out, it can be concluded that there is a significant contribution from learning Strategy, memory and metacognition to the learning achievement upper students in elementary schools in Banten Province where the results from the Coefficients table, obtained a significance value of 0.000 t_table of 1.293, so it can be concluded that the Learning Strategy (X3), Memory (X4) and Metacognitive (X5) variables contribute simultaneously to the Learning Achievement variable (Y) while Attitude (X1) and Learning Motivation (X2) do not contribute to Learning Achievement ( Y) . The correlation or relationship value (R) is 0.795. From this output, a coefficient of determination (R2) of 0.632 is obtained, which means that the influence of the independent variables Learning Strategy (X3), Memory (X4) and Metacognition (X5) on the dependent variable Learning Achievement (Y) is 63.2 %. Meanwhile, the remaining 36.8% is influenced by other variables not studied.

Retrospective Analysis of Circumstances of Falls and Related Injuries across Levels of Care in Older Adult Retirement Home Facilities.

Towards developing more effective interventions for fall-related injuries, this study analysed a novel database from six retirement home facilities over a 4-year period comprising 1,877 fallers and 12,445 falls. Falls were characterized based on location, activity, injury site, and type, and the database was stratified across four levels of care: Independent Living, Retirement Care, Assisted Care, and Memory care. Falls most occurred within the bedroom (62.8%), and during unknown (38.1%), walking (20.2%), and transfer tasks (14.6%). Approximately one in three (37%) of all falls resulted in an injury, most commonly involving the upper limb (31.8%), head (26.3%), and lower limb (22.2%), resulting in skin tears (35.3%), aches/pains (29.1%), or bruises (28.0%). While fall location, activity, and injury site were different across levels of care, injury type was not. The data from this study can assist in targeting fall-related injury prevention strategies across levels of care within retirement facilities.

Hong-Ou-Mandel interference of single-photon-level pulses stored in independent room-temperature quantum memories

Quantum repeater networks require independent absorptive quantum memories capable of storing and retrieving indistinguishable photons to perform high-repetition entanglement swapping operations. The ability to perform these coherent operations at room temperature is of prime importance for the realization of scalable quantum networks. We perform Hong-Ou-Mandel (HOM) interference between photonic polarization states and single-photon-level pulses stored and retrieved from two sets of independent room-temperature quantum memories. We show that the storage and retrieval of polarization states from quantum memories does not degrade the HOM visibility for few-photon-level polarization states in a dual-rail configuration. For single-photon-level pulses, we measure the HOM visibility with various levels of background in a single polarization, single-rail QM, and investigate its dependence on the signal-to-background ratio. We obtain an HOM visibility of 43%, compared to the 48% no-memory limit of our set-up. These results allow us to estimate a 33% visibility for polarization qubits under the same conditions. These demonstrations lay the groundwork for future applications using large-scale memory-assisted quantum networks.

Clinical Manifestations.

Racial and ethnic disparities in ADRD diagnosis are well documented but remain poorly understood. Clinical ADRD diagnosis relies on neuropsychological assessment, which is interpreted by comparing individual performance to demographically adjusted reference group norms. Differential use of demographic adjustment procedures and sources of normative data may compromise diagnostic accuracy of neuropsychological findings in diverse individuals. Using two different demographic adjustment approaches derived from two distinct normative samples, we generated cognitive impairment categories and evaluated their associations with longitudinal trajectories of independent executive functioning and memory scores. Participants were 717 older adults (age: 77.5±6.9, 56% women) enrolled in the Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) study who completed up to 3 annual visits. The main predictor was cognitive impairment category (no impairment, single-domain, multiple-domain) based on Uniform Data Set Neuropsychological Battery (UDSNB) scores at baseline. UDSNB scores were either demographically unadjusted or adjusted (age, sex, education, and race/ethnicity) using data from the National Alzheimer's Coordinating Center (NACC; N = 8313, 16% Black, 84% White) or KHANDLE (N = 397, 26% Asian, 18% Black, 30% LatinX, 26% White) samples. Linear mixed effect models were used to examine the associations between differentially derived cognitive impairment categories with longitudinal changes on episodic memory and executive functioning composites from an independent cognitive battery (Spanish and English Neuropsychological Assessment Scales, SENAS). Cognitive impairment categories derived from the KHANDLE sample were more strongly associated in all four racial and ethnic groups with longitudinal decline on episodic memory compared to the NACC sample-derived categories (Figure 1). Similar, albeit attenuated, effects were observed in relation to longitudinal decline on the executive function composite. There were no clear differences in the associations between cognitive impairment categories with executive functioning or memory decline using demographically adjusted vs. unadjusted scores. Our results highlight the need for greater representation of diverse individuals in normative samples underlining the limitations of the NACC-derived normative algorithms for cognitive assessment of individuals from diverse populations. Further investigations into the clinical value of the practice of demographic adjustments are also warranted.

Facilitating Clinical Use of the Amsterdam Instrumental Activities of Daily Living Questionnaire: Normative Data and Diagnostic Cutoff Values

AbstractBackgroundThe Amsterdam Instrumental Activities of Daily Living Questionnaire(A‐IADL‐Q) measures impairments in everyday functioning in the context of dementia. To facilitate interpretation and clinical implementation of the A‐IADL‐Q, we aimed to generate demographically‐adjusted norm scores and a clinical cutoff value.MethodCross‐sectional data were used from Dutch Brain Research Registry(N = 1,064; mean (M) age = 62±11year; 69.5% female) European Medial Information Framework‐Alzheimer’s Disease(EMIF‐AD) 90+ (N = 63; Mage = 92±2year; 52.4% female), and European Prevention of Alzheimer’s Dementia Longitudinal Cohort Study(EPAD‐LCS)(N = 300; Mage = 62±7year; 71.7% female)(Table 1). Norm scores were generated using a I) linear regression model approach; and II) Box‐Cox Power Exponential(BCPE) model approach (the latter falling within the generalized model for location shape and size(GAMLSS) framework). For both methods, different combinations of the variables age, sex and education were tested (including polynomial age and age*education interaction) and the best model was selected based on the lowest Aikake Information Criteria(AIC) value. Next, diagnostic contrast was generated using an independent memory clinic sample (the Amsterdam Dementia Cohort(ADC)(N = 2,123, Mage = 64±8year, 46.1% female)(Table 1), including people with dementia (both Alzheimer’s Disease(AD) and non‐AD). The optimal cut‐off value for cognitively healthy versus dementia was determined, and corresponding diagnostic accuracy was calculated using area under the receiver operating curves(AUC‐ROC). This optimal cutoff was then applied to different diagnostic contrasts, including mild cognitive impairment(MCI) and subjective cognitive decline(SCD).ResultThe best suitable normative model was the linear regression model, including cubic polynomial for age, education, and age*education interaction. This model was considered superior to the best BCPE model, which had infinitely large residuals due to bad model fit. The optimal cutoff to distinguish normal functioning from dementia, based on bootstrap average estimates of the Youden Index, was 1.82 standard deviation(SD) below the population mean, resulting in an AUC of 0.97[95%CI 0.98‐0.98]). Normative data are made publicly available in an online tool(Fig.1).ConclusionWe established demographically adjusted norm scores for the A‐IADL‐Q using a large representative sample, and provided an optimal cutoff value for cognitively healthy versus dementia, with outstanding accuracy. Our results, captured in the online tool, can help clinicians to interpret A‐IADL‐Q scores more easily by allowing comparison of functioning against a reference population.

How is preschoolers' memory performance related to parental elaboration during reminiscence?

AbstractThe influence of parental reminiscing style – how parents discuss past events with their child – on preschoolers' independent memory skills (outside of parent–child conversations) and the processes involved in this effect (memory consolidation vs. development of strategies) is far from clear. To test this, 50 parent–child dyads (MChildAge = 52.12) were recruited. Parents' level of elaboration during reminiscing was measured while children completed tasks assessing their memories about two standardized events, one they had discussed with their parent and one they had not discussed, and an episodic task requiring the memorization of new information. Children of high‐elaborative parents performed better than children of low‐elaborative parents on the recognition test for the non‐discussed event and for the episodic memory task. This suggests that parental elaboration is related to preschoolers' independent memory skills and that its effect might exceed memory consolidation and depend on processes like the acquisition of strategies.

Preservation of Sensitive Data Using Multi-Level Blockchain-based Secured Framework for Edge Network Devices

The proliferation of IoT devices has influenced end users in several aspects. Yottabytes (YB) of information are being produced in the IoT environs because of the ever-increasing utilization capacity of the Internet. Since sensitive information, as well as privacy problems, always seem to be an unsolved problem, even with best-in-class in-formation governance standards, it is difficult to bolster defensive security capabilities. Secure data sharing across disparate systems is made possible by blockchain technology, which operates on a decentralized computing paradigm. In the ever-changing IoT environments, blockchain technology provides irreversibility (immutability) usage across a wide range of services and use cases. Therefore, blockchain technology can be leveraged to securely hold private information, even in the dynamicity context of the IoT. However, as the rate of change in IoT networks accelerates, every potential weak point in the system is exposed, making it more challenging to keep sensitive data se-cure. In this study, we adopted a Multi-level Blockchain-based Secured Framework (M-BSF) to provide multi-level protection for sensitive data in the face of threats to IoT-based networking systems. The envisioned M-BSF framework incorporates edge-level, fog-level, and cloud-level security. At edge- and fog-level security, baby kyber and scaling kyber cryptosystems are applied to ensure data preservation. Kyber is a cryptosystem scheme that adopts public-key encryption and private-key decryption processes. Each block of the blockchain uses the cloud-based Argon-2di hashing method for cloud-level data storage, providing the highest level of confidentiality. Argon-2di is a stable hashing algorithm that uses a hybrid approach to access the memory that relied on dependent and independent memory features. Based on the attack-resistant rate (> 96%), computational cost (in time), and other main metrics, the proposed M-BSF security architecture appears to be an acceptable alternative to the current methodologies.

A two-step workflow based on plasma p-tau217 to screen for amyloid β positivity with further confirmatory testing only in uncertain cases

Cost-effective strategies for identifying amyloid-β (Aβ) positivity in patients with cognitive impairment are urgently needed with recent approvals of anti-Aβ immunotherapies for Alzheimer’s disease (AD). Blood biomarkers can accurately detect AD pathology, but it is unclear whether their incorporation into a full diagnostic workflow can reduce the number of confirmatory cerebrospinal fluid (CSF) or positron emission tomography (PET) tests needed while accurately classifying patients. We evaluated a two-step workflow for determining Aβ-PET status in patients with mild cognitive impairment (MCI) from two independent memory clinic-based cohorts (n = 348). A blood-based model including plasma tau protein 217 (p-tau217), age and APOE ε4 status was developed in BioFINDER-1 (area under the curve (AUC) = 89.3%) and validated in BioFINDER-2 (AUC = 94.3%). In step 1, the blood-based model was used to stratify the patients into low, intermediate or high risk of Aβ-PET positivity. In step 2, we assumed referral only of intermediate-risk patients to CSF Aβ42/Aβ40 testing, whereas step 1 alone determined Aβ-status for low- and high-risk groups. Depending on whether lenient, moderate or stringent thresholds were used in step 1, the two-step workflow overall accuracy for detecting Aβ-PET status was 88.2%, 90.5% and 92.0%, respectively, while reducing the number of necessary CSF tests by 85.9%, 72.7% and 61.2%, respectively. In secondary analyses, an adapted version of the BioFINDER-1 model led to successful validation of the two-step workflow with a different plasma p-tau217 immunoassay in patients with cognitive impairment from the TRIAD cohort (n = 84). In conclusion, using a plasma p-tau217-based model for risk stratification of patients with MCI can substantially reduce the need for confirmatory testing while accurately classifying patients, offering a cost-effective strategy to detect AD in memory clinic settings.

DADE-DQN: Dual Action and Dual Environment Deep Q-Network for Enhancing Stock Trading Strategy

Deep reinforcement learning (DRL) has attracted strong interest since AlphaGo beat human professionals, and its applications in stock trading are widespread. In this paper, an enhanced stock trading strategy called Dual Action and Dual Environment Deep Q-Network (DADE-DQN) for profit and risk reduction is proposed. Our approach incorporates several key highlights. First, to achieve a better balance between exploration and exploitation, a dual-action selection and dual-environment mechanism are incorporated into our DQN framework. Second, our approach optimizes the utilization of storage transitions by utilizing independent replay memories and performing dual mini-batch updates, leading to faster convergence and more efficient learning. Third, a novel deep network structure that incorporates Long Short-Term Memory (LSTM) and attention mechanisms is introduced, thereby improving the network’s ability to capture essential features and patterns. In addition, an innovative feature selection method is presented to efficiently enhance the input data by utilizing mutual information to identify and eliminate irrelevant features. Evaluation on six datasets shows that our DADE-DQN algorithm outperforms multiple DRL-based strategies (TDQN, DQN-Pattern, DQN-Vanilla) and traditional strategies (B&H, S&H, MR, TF). For example, on the KS11 dataset, the DADE-DQN strategy has achieved an impressive cumulative return of 79.43% and a Sharpe ratio of 2.21, outperforming all other methods. These experimental results demonstrate the performance of our approach in enhancing stock trading strategies.

Mnemonic Discrimination Performance in a Memory Clinic: A Pilot Study.

Mnemonic discrimination is the behavioral ability stemming from pattern separation, which is the neural process of establishing independent and non-overlapping new memories. Over the past two decades, its assessment in various populations has contributed to a better conceptual understanding of age-related memory decline. To assess the clinical relevance of mnemonic discrimination in the memory clinics setting. This retrospective study was performed in 90 patients with a Mini-Mental State Examination (MMSE)>18 who consulted our memory clinic for the first time. All patients were tested with the Mnemonic Similarity Task, a freely available computerized test. Global cognitive function, executive function, visuoconstructional abilities, and verbal and visual episodic memory were also collected, together with the diagnosis after the initial clinical assessment (subjective cognitive complaint [SCC], mild cognitive impairment [MCI], or mild dementia). Mnemonic discrimination performance was correlated with global cognitive function, executive function, and visual and verbal episodic memory scores, independent of age. It discriminated patients with SCC from those with MCI (amnestic or non-amnestic) with moderate accuracy (AUC = 0.77-0.78), similar to MMSE and the Frontal Assessment Battery (AUC = 0.74-0.84). Mnemonic discrimination performance did not distinguish between amnestic and non-amnestic MCI and the variability of the measure was important within groups. Mnemonic discrimination performance involves many cognitive domains and discriminates between patients with SCC and MCI with performance equivalent to "paper-and-pencil" screening tests. Further dedicated prospective studies will determine whether this task is of interest beyond research purposes, as a diagnostic or screening tool in primary care.

Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models

Short-term photovoltaic (PV) power forecasting is essential for integrating renewable energy sources into the grid as it provides accurate and timely information on the expected output of PV systems. Deep learning (DL) networks have shown promising results in this area, but depending on the weather conditions and the particularities of each PV system, different DL architectures may perform best. This paper proposes a meta-learning method to improve one-hour-ahead deterministic forecasts of PV systems by dynamically blending the base forecasts of multiple DL models to learn under what conditions each model performs best. Four base models of different long short-term memory architectures are used to produce PV production forecasts without using numerical weather predictions, with the objective to enhance the generalizability of the proposed solution. The accuracy of the meta-learner is evaluated using three rooftop PV systems in Lisbon, Portugal. Results indicate that different base models perform best at different PV plants, and meta-learning can improve accuracy by up to 5% over the most accurate base model per plant and up to 4.5% over the equal-weighted combination of the base forecasts. These improvements are statistically significant and even larger during peak production hours.

Network Monitoring on Multi-Pipe Switches

Programmable switches have been widely used to design network monitoring solutions that operate in the fast data-plane level, e.g., detecting heavy hitters, super-spreaders, computing flow size distributions and their entropy. Existing works assume packets access the same memory region in a switch. However, high-speed ASIC switches deploy multiple packet processing pipes, each equipped with its own independent memory. In this work, we first quantify the accuracy degradation due to splitting a monitoring data structure across multiple pipes (e.g., up to 3000x worse flow-size estimation average error). We then present PipeCache, a system that adapts existing data-plane mechanisms to multi-pipe switches by storing monitoring information for a traffic class into a single pipe. PipeCache stores monitoring information into a cache and piggybacks this information onto existing data packets to the correct pipe. Our implementation shows a 2-20x memory reduction to achieve an accuracy similar to single-pipe deployments.

Weighted l1‐Penalized Corrected Quantile Regression for High‐Dimensional Temporally Dependent Measurement Errors

This article derives some large sample properties of weighted ‐penalized corrected quantile estimators of the regression parameter vector in a high‐dimensional errors in variables (EIVs) linear regression model. In this model, the number of predictors depends on the sample size and tends to infinity, generally at a faster rate than , as tends to infinity. Moreover, the measurement errors in the covariates are assumed to have linear stationary temporal dependence and known Laplace marginal distribution while the regression errors are assumed to be independent non‐identically distributed random variables having possibly heavy tails. The article discusses some rates of consistency of these estimators, a model consistency result and an appropriate data adaptive algorithm for obtaining a suitable choice of weights. A simulation study assesses the finite sample performance of some of the proposed estimators. This article also contains analogs of Massart's inequality for independent and short memory moving average predictors, which is instrumental in establishing the said consistency rates of the above mentioned estimators in the current setup of high dimensional EIVs regression models.

Pattern of CD8+ tissue resident memory T cells between local recurrence and distant metastasis in nasopharyngeal carcinoma.

e15161 Background: CD8+ T lymphocytes are believed to be the major anti-tumor immune subsets. Consequently, most cancer immunotherapeutic approaches seek to amplify cytotoxic T lymphocytes specific to malignant cells. A recently identified subpopulation of memory CD8+ T cells, named tissue-resident memory T (Trm) cells, persists in non-lymphoid tissues, including solid tumors. This T-cell subset is considered an independent memory T-cell lineage with cytotoxic features. However, their function, spatial location and influence on disease-related outcomes (local recurrence [LR], distant metastasis [DM] in nasopharyngeal carcinoma (NPC), a disease entity characterized by substantial immune infiltrate, remain poorly understood. We aim to close this gap through investigation on the abundance and spatial localization of CD8+ Trm in relation to tumor site (primary or recurrence [LR and DM]). Methods: This retrospective cohort analysis based on 20 NPC patient samples: primary (initial diagnosis; [I; n = 19], recurrence ([LR; n = 14] and [DM; n = 10]). We accessed the spatial localization in tumor tissues within tissue-segmented classifications of either cancer islands (pan-cytokeratin–positive [CK+] areas) or stromal areas (CK– areas). Tumor infiltrating lymphocytes (TILs) among patients from I, LR and DM group was quantified through multiplexed 5-colour opal staining using biomarkers against CD8, CD103, IL17, CK plus DAPI (nuclear cell counterstain). Results: While total CD8+ TILs (total tumor) as well as their spatially localized cell densities (tumor islet [TI] vs. tumor stroma [TS]) did not show any significant changes among I, LR and DM patient groups, a higher proportion of CD8+ TILs in the TI co-express the CD103 molecule, an indication of CD8+ Trm (CD103+CD8+) infiltration (mean CD103+CD8+/CD8+ in TI: 32.1% in I group, 44.8% in LR group, 46.0% in DM group; mean CD103+CD8+/CD8+ in TS: 19.0% in I group, 26.2% in LR group 26.2% in DM group). Notably, we observed that such augmentation only reached statistically significant within TI from patient in LR group (p = 0.0451). Furthermore, we found that CD8+ Trm in the LR group expressed higher level of cytokine IL-17, which was ~2.1-fold (p = 0.0788) and 3.6-fold upregulated (p = 0.0285) as compared to I and DM, respectively. Conclusions: Cancer islet–localized CD8+ TILs are composed of CD103+ Trms, which make up nearly 45% the total CD8+ T cell population within LR tumors of NPC patients, highlighting spatial localization of CD103+CD8+ Trms as a key phenotype of CD8+ TIL subset. The CD8+ Trm expressed with increased IL17 suggests that enrichment of this T-cell subset might exerts cytotoxic functions. Our results demonstrate that CD8+ Trm had a greater likelihood as an indicator of LR risk for NPC patients and help identify patient cohorts likely to benefit from immunotherapy.

Semantic associates create retroactive interference on an independent spatial memory task.

Semantic similarity between stimuli can lead to false memories and can also potentially cause retroactive interference (RI) for veridical memories. Here, participants first learned spatial locations for "critical" words that reliably produce false memories in the Deese-Roediger-McDermott paradigm. Next, participants centrally viewed words that were semantically associated with half of the critical words. Finally, participants retrieved the spatial locations for the critical words. We found that spatial memory was worse for critical words whose semantic associates were shown versus not shown, suggesting that semantic relatedness caused RI. This effect occurred in three experiments when the interfering information was presented shortly before the spatial test but not when there was a 1-hour delay before the test, nor when the order of the spatial learning and associate learning phases were reversed. These findings suggest that RI can occur solely via semantic associates when all relevant responses and no distracting responses were available at retrieval. We consider these findings to be an example of cue overload, whereby cues can be overloaded indirectly via semantic associates, and to support the importance of both semantic similarity and temporal context in RI. (PsycInfo Database Record (c) 2022 APA, all rights reserved).