Conventional Memory Research Articles

4D Printing of Ultra‐High Performance Shape Memory Polymer for Space Applications

Developing thermoplastic polyimide (TPI), capable of handling space conditions, through 4D printing is challenging due to its high melting temperature and inherent viscosity. This study presents 4D printing of TPI for shape memory investigation under repetitive cycles for the first time, exploring its potential for self‐deployable hinges in space devices. 4D‐printed TPI exhibits outstanding shape memory effect (SME) with shape fixity (Rf) up to 100% and shape recovery (Rr) of 100% in first cycle. Rf is noted to be increasing up to third cycle and then fixed to 100% up to tenth cycle, while Rr shows a decreasing trend in subsequent cycle with a drop of 37% in tenth cycle. Moreover, it exhibits extremely high glass‐transition temperature, Tg = 263.10 °C, degradation temperature, Td = 520 °C, and storage modulus of 1600 MPa. Among existing high‐performance (HP) and conventional shape memory polymers (SMPs), 3D‐printed TPI exhibits superior performance. Tg of the TPI is found to be 66.52%, 107.16%, and 62.41%, higher than existing HP‐SMPs, polyether ether ketone (Tg = 158 °C), polyamide (Tg = 127 °C), and polyether ketone ketone (Tg = 162 °C), respectively. This investigation reveals a novel characteristic, the SME, of 4D‐printed TPI with ultra‐high Tg and Td, demonstrating suitability for self‐deployable hinges, contributing to materials engineering and 4D printing.

Enabling Energy-Efficient Deployment of Large Language Models on Memristor Crossbar: A Synergy of Large and Small.

Large language models (LLMs) have garnered substantial attention due to their promising applications in diverse domains. Nevertheless, the increasing size of LLMs comes with a significant surge in the computational requirements for training and deployment. Memristor crossbars have emerged as a promising solution, which demonstrated a small footprint and remarkably high energy efficiency in computer vision (CV) models. Memristors possess higher density compared to conventional memory technologies, making them highly suitable for effectively managing the extreme model size associated with LLMs. However, deploying LLMs on memristor crossbars faces three major challenges. Firstly, the size of LLMs increases rapidly, already surpassing the capabilities of state-of-the-art memristor chips. Secondly, LLMs often incorporate multi-head attention blocks, which involve non-weight stationary multiplications that traditional memristor crossbars cannot support. Third, while memristor crossbars excel at performing linear operations, they are not capable of executing complex nonlinear operations in LLM such as softmax and layer normalization. To address these challenges, we present a novel architecture for the memristor crossbar that enables the deployment of state-of-the-art LLM on a single chip or package, eliminating the energy and time inefficiencies associated with off-chip communication. Our testing on BERT Large showed negligible accuracy loss. Compared to traditional memristor crossbars, our architecture achieves enhancements of up to 39× in area overhead and 18× in energy consumption. Compared to modern TPU/GPU systems, our architecture demonstrates at least a 68× reduction in the area-delay product and a significant 69% energy consumption reduction.

Low power process tolerant nano cache memory for military applications

ABSTRACT Rapid growth in high-performance battery-powered computing applications, Internet of Things (IoT) and artificial intelligence (AI), has raised the need for low-power integrated circuits. Particularly in military applications, wireless sensor nodes are used to detect border intrusion, weapon attacks, biometric wearables, internet of battlefield things (IoBT), etc. The computing and security devices utilised by the soldiers are powered by batteries. To increase the life and performance of these devices, the integrated circuits (IC) used within these devices need careful design. Particularly, an appropriate memory needs more attention because the design of memory has a substantial impact on IC performance as it occupies most of the chip space. In this article, a novel 8T transistor low-power cache memory cell is proposed and simulations for nominal chirality and dual chirality are done using HSPICE-compatible CNFET (carbon nanotube field effect transistor) technology. The power consumption is minimized in dual chirality case than nominal chirality. But still the power saving percentage of the proposed cache memory cell remains good compared to conventional memory structures. Thus the proposed cache memory cell provides low power, delay and energy apart from being process tolerant, thus proving that it is compatible for warfare and military applications.

Graphene-enhanced ferroelectric domain wall high-output memristor

Recent studies on memristive materials and technologies have expanded beyond conventional memory elements, driven by their potential application in novel information processing concepts. Among these materials, conductive domain walls in ferroics are especially promising, offering conductive tunability suitable for reconfigurable multi-state devices. However, challenges such as domain stability, time-dependent conductivity, and low current output have impeded progress in the field. Here, we study the graphene/Pb(Zr,Ti)O3/SrRuO3 system, which demonstrates robust domain wall conduction up to 100 nA/μm2 for 2 V bias, while addressing the critical issue of stability of switched domains. The introduction of graphene electrodes enhances low-voltage stochastic domain formation with limited domain expansion that promotes the emergence of multi-domain states. The developed micrometer sized capacitor devices enable electrically programmable multiple distinct conduction states with robust retention combined with high current output and low operation voltage. These features are highly desirable for memristors and mark the significant potential of domain wall electronics for neuromorphic computing.

Multi-Way adaptive Time Aware LSTM for irregularly collected sequential ICU data

In personalized predictive medicine, accurately modeling a patient’s illness and care processes is essential, given their inherent long-term temporal dependencies. However, electronic medical records contain episodic and irregularly timed data due to patients visiting hospitals based on treatment needs, resulting in unique patterns for each hospital stay. Consequently, when constructing a personalized predictive model, it is crucial to consider these factors in order to accurately capture the patient’s health journey.To address this challenge, we present a novel deep dynamic memory neural network called Multi-Way adaptive Time Aware LSTM (MWTA-LSTM). The primary objective of MWTA-LSTM is to leverage medical records, memorize illness trajectories and care processes, estimate current illness states, and predict future risks, thereby providing a high level of precision and predictive power. To enhance its capabilities, MWTA-LSTM extends the conventional Long Short-Term Memory (LSTM) model in two key ways. Firstly, it incorporates frequency measurement and the most recent observation(last observation) to enhance personalized predictive modeling of patient illnesses, enabling a more accurate understanding of the patient’s condition. Secondly, it parameterizes the cell state to handle irregular timing effectively, utilizing both frequency measurement and elapsed times. Furthermore, the model capitalizes on both to comprehend the impact of interventions on the course of illness on the cell state, facilitating the memorization of illness courses and improving its ability to capture the temporal dynamics of healthcare data, accommodating variations and irregularities in event and observation timing. Lastly, we introduce a novel adaptive pooling strategy that specifically targets and resolves outlier issues that can potentially occur during the analysis of EHR data. By incorporating these features, MWTA-LSTM significantly improves its ability to capture the temporal dynamics of healthcare data, accommodating variations and irregularities in event and observation timing.We validate the effectiveness of our proposed model through empirical experiments conducted on two real-world clinical datasets and three real-world time series datasets. Our results demonstrate the superiority of MWTA-LSTM over current state-of-the-art models and other robust baselines. This showcases the potential of MWTA-LSTM in advancing personalized predictive medicine by offering a more accurate and comprehensive approach to modeling patient health trajectories.

Dynamic Prediction for Pollutant Emissions of Coal-fired Power Plant Based on CNN-LSTM-Attention

Abstract The prediction accuracy of pollutant emissions by using traditional modeling methods is unsatisfactory in dynamic conditions. To overcome the problem, data-driven modeling was introduced to build the dynamic model of pollutant emissions of power plants in this paper. Combining with the running data of a 300MW circulating fluidized bed (CFB) unit, the dynamic prediction models of SO2 and NO x emissions were established respectively by using conventional neural network-long short-term memory and attention mechanism (CNN-LSTM-Attention). Moreover, LSSVM, LSTM and CNN-LSTM were introduced for comparison to demonstrate the superiority of CNN-LSTM-Attention model respectively. Simulation results indicate that model can imitate change trend of actual data with high accuracy over a long period of time. Compared with LSSVM, LSTM and CNN-LSTM, the proposed model has better modeling performance under different load conditions. This work provides certain guidance for the application of deep learning in the industrial field.

Barrier Polarity Reversal Based on Interfacial Modification of Au Nanoparticles for Nonvolatile Multilevel Memory and Optoelectronic Synapses.

Optoelectronic synaptic devices, integrating light sensing and information processing capabilities, have emerged as advantageous tools for the implementation of visual neuromorphic computing. However, the transient light-triggered response characteristic typically results in unstable memory retention times and restricted current response ranges, posing significant challenges to the development and practical application of neural network systems. In response to these limitations, this study developed a nonvolatile optoelectronic memory based on the indium tin oxide (ITO)/Au nanoparticles (NPs)/amorphous Ga2O3 (a-Ga2O3)/Pt structure. Unlike conventional optoelectronic memories, this device features a modification with Au NPs that markedly enhances the Schottky barrier height at the interface. Au NPs function as a charge-trapping layer for sensitive and large-scale modulation of the barrier by the light field, thereby enabling the nonvolatile reversal of the device's barrier polarity. This innovative approach enables controllable multilevel data storage with an ultra large on/off ratio (∼104) and excellent retention capability exceeding 12,000 s. Additionally, the device emulates essential synaptic functions and demonstrates potential application values in visual weak signal perception and image memory. This study introduces a mechanism for Schottky barrier polarity control and presents a promising strategy for the development of future high-performance integrated devices and optoelectronic synaptic elements.

Tailoring magnetism in chromium-doped zinc cobalt ferrite nanostructure for advanced spintronic memory devices

Soft magnetic ferrites serve as a crucial component in a wide array of sensing and actuating applications across various industries, including consumer electronics, automotive systems, spintronics, and more. These materials also find utility in spintronics, particularly for next-generation magnetic random-access memory (MRAM) due to their excellent inductive properties, which enhance the performance and scalability of spintronic memory devices. This study focuses on fine-tuning soft magnetic ferrites for inductive applications by doping them with Cr ions. By utilizing advanced experimental techniques such as the vibrating sample magnetometer (VSM) for bulk magnetometry and X-ray magnetic circular dichroism (XMCD) alongside X-ray absorption spectroscopy for atomic studies, the research aims to investigate the structural, electronic, and magnetic properties of the nanoferrites. Addition of doping in the soft ferrites observes reduction in the general magnetic parameters, with the only exception being coercivity, which decreases from 381 Oe to 275 Oe as the doping concentration increases from x = 0 to x = 0.02, and then increases to 350 Oe at x = 0.03. Through such precise tuning of the nanoparticles, this study aims to investigate the controllable soft magnetic properties of the nanoferrites, thereby paving the way for fast access times, non-volatility, and low energy consumption, presenting a compelling alternative to conventional memory technologies.

Human breastmilk memory T cells throughout lactation manifest activated tissue-oriented profile with prominent regulation.

Breastfeeding provides important immunological benefits to the neonate, but how the different immunoactive components in breastmilk contribute to immunity remains poorly understood. Here, we characterized human breastmilk T cells using single-cell RNA-Seq and flow cytometry. Breastmilk contained predominantly memory T cells, with expression of immune signaling genes, high proliferation, and an effector Th1/cytotoxic profile with high cytokine production capacities. Elevated activation was balanced by an enriched Treg population and immune regulatory markers in conventional memory T cells. Gene and surface expression of tissue-residency markers indicate that breastmilk T cells represented tissue-adapted rather than circulatory T cells. In addition, breastmilk T cells had a broad homing profile and higher activation markers in these migratory subsets. The partly overlapping transcriptome profile between breastmilk and breast tissue T cells, particularly cytotoxic T cells, might support a role in local immune defense in the mammary gland. However, unique features of breastmilk, such as Tregs, might imply an additional role in neonatal immune support. We found some correlations between the breastmilk T cell profile and clinical parameters, most notably with maternal and household factors. Together, our data suggest that breastmilk contains an adapted T cell population that exerts their function in specific tissue sites.

Predicting agricultural and meteorological droughts using Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM).

Drought is an extended shortage of rainfall resulting in water scarcity and affecting a region's social and economic conditions through environmental deterioration. Its adverse environmental effects can be minimised by timely prediction. Drought detection uses only ground observation stations, but satellite-based supervision scans huge land mass stretches and offers highly effective monitoring. This paper puts forward a novel drought monitoring system using satellite imagery by considering the effects of droughts that devastated agriculture in Thanjavur district, Tamil Nadu, between 2000 and 2022. The proposed method uses Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM) to forecast meteorological and agricultural droughts. It employs Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data precipitation index datasets, MODIS 11A1 temperature index, and MODIS 13Q1 vegetation index. It extracts the time series data from satellite images using trend and seasonal patterns and smoothens them using Holt Winter alpha, beta, and gamma parameters. Finally, an effective drought prediction procedure is developed using Conv2D-LSTM to calculate the spatiotemporal correlation amongst drought indices. The HW-Conv2DLSTM offers a better R2 value of 0.97. It holds promise as an effective computer-assisted strategy to predict droughts and maintain agricultural productivity, which is vital to feed the ever-increasing human population.

Synthesis and Anisotropic Memristive Behavior of Borophene Nanosheets

Neuromorphic computing, marked by its parallel computational abilities and low power usage, has become pivotal in advancing artificial intelligence. However, the advancement of neuromorphic computing has faced significant obstacles due to the performance limitations of traditional memory devices struggling with high power consumption and limited reliability. Two‐dimensional (2D) materials have been extensively investigated as high‐performance memristive materials, but they are often restricted by fixed memristive properties, which complicate circuit design and limit flexibility. Here, we report that multilayer borophene nanosheets represent a breakthrough material, displaying anisotropic variable memristive properties. The nanosheets, comprising semiconductor α’‐4H‐borophene sheets and metal β12‐borophene sheets, have been synthesized on aluminum foil surface through chemical vapor deposition (CVD) method. The multilayer borophene nanosheets exhibit volatile memory behavior in the vertical direction and non‐volatile memory behavior in the planar direction. This innovative class of 2D nanosheets not only overcomes the limitations of conventional memory devices but also expands the potential applications of borophene‐based memories in information storage and in‐memory computing.

Synthesis and Anisotropic Memristive Behavior of Borophene Nanosheets.

Neuromorphic computing, marked by its parallel computational abilities and low power usage, has become pivotal in advancing artificial intelligence. However, the advancement of neuromorphic computing has faced significant obstacles due to the performance limitations of traditional memory devices struggling with high power consumption and limited reliability. Two-dimensional (2D) materials have been extensively investigated as high-performance memristive materials, but they are often restricted by fixed memristive properties, which complicate circuit design and limit flexibility. Here, we report that multilayer borophene nanosheets represent a breakthrough material, displaying anisotropic variable memristive properties. The nanosheets, comprising semiconductor α'-4H-borophene sheets and metal β12-borophene sheets, have been synthesized on aluminum foil surface through chemical vapor deposition (CVD) method. The multilayer borophene nanosheets exhibit volatile memory behavior in the vertical direction and non-volatile memory behavior in the planar direction. This innovative class of 2D nanosheets not only overcomes the limitations of conventional memory devices but also expands the potential applications of borophene-based memories in information storage and in-memory computing.

Inflammatory Memory in Epidermal Stem Cells - A New Strategy for Recurrent Inflammatory Skin Diseases.

The ability of the skin to "remember" has been a potential mechanism for studying recurrent skin diseases. While it has been thought that the ability to retain past encounters is the prerogative of immune cells, it has recently been discovered that skin tissue stem cells can also take on this task. Epithelial stem cells undergoing inflammation retain their "memory" through epigenetic reprogramming and exhibit rapid epithelialization and epidermal proliferation upon secondary stimulation. This is a non-specific memory modality independent of conventional immune memory, in which histone modifications (acetylation and methylation) and specific transcription factors (AP-1 and STAT3) are involved in the establishment of inflammatory memories, and AIM2/Caspase-1/IL-1β mainly performs the rapid effects of memory. This finding is intriguing for addressing recurrent inflammatory skin diseases, which may explain the fixed-site recurrence of inflammatory skin diseases and develop new therapeutic strategies in the future. However, more research is still needed to decipher the mysteries of memory.

Improved memory truncation scheme for quasi-adiabatic propagator path integral via influence functional renormalization.

Accurately simulating non-Markovian quantum dynamics in system-bath coupled problems remains challenging. In this work, we present a novel memory truncation scheme for the iterative quasi-adiabatic propagator path integral (iQuAPI) method to improve accuracy. Conventional memory truncation in iQuAPI discards all influence functional beyond a certain time interval, which is not effective for problems with a long memory time. Our proposed scheme selectively retains the most significant parts of the influence functional using the density matrix renormalization group algorithm. We validate the effectiveness of our scheme through simulations of the spin-boson model across various parameter sets, demonstrating faster convergence and improved accuracy compared to the conventional scheme. Our findings suggest that the new memory truncation scheme significantly advances the capabilities of iQuAPI for problems with a long memory time.

A carbon electrode approach for TiO2-write-once-read-many resistive memories

Nanomaterials are a potential alternative to conventional random resistive access memory (ReRAM) in non-volatile memory (NVM) due to their unique electrical and physical properties. Nevertheless, obtaining a specific NVM characteristic [particularly Write-Once-Read-Many (WORM)] using carbon-based electrodes necessitates further assessment. This study proposed a reinvented methodology for TiO2-based devices to produce WORM memory behavior using the doctor blade method. A carbon/graphite counter electrode (Gr/Cb) was fabricated and characterized for TiO2-based resistive switching devices. Consequently, the Gr/Cb electrode device quickly transitioned from a high to low resistance state, suggesting WORM memory behavior. Approximately 66.7 % improvement (∼1.8 V–∼0.6 V) was observed in the required applied voltage when the active area was reduced, indicating lower power consumption. Overall, this study provided evidence of the approach's feasibility in practical applications, demonstrating lower costs and simplicity. The structure also highlighted the importance of understanding the WORM behavior for effectively utilizing carbon-based electrodes in non-volatile memories.

HfO2-based resistive random access memory with an ultrahigh switching ratio

Resistive Random Access Memory (RRAM) is considered one of the most promising candidates for big data storage. By using atomic layer deposition and magnetron sputtering, HfO2 thin films were prepared on ITO first, which exhibited good resistive switching (RS) characteristics in the structure of Ag/HfO2/ITO. By analyzing the RS mechanism, it is found that both metal conductive filaments and oxygen vacancy conductive filaments coexisted and Sn ion in ITO can influence the retention of RRAM. Furthermore, a device in the structure of Ag/HfO2/Pt was proposed and prepared, which exhibited excellent RS characteristics, including an ultrahigh switching ratio averaging up to 108 and low operating voltage. It is concluded that the difference in the work function between the top and bottom electrodes contributes to improving the switching ratio, reducing the operating voltage. In addition, the Ag/HfO2/Pt device is similar to the Ag/HfO2-based threshold switching selector in the structure and in characteristics of high switching ratio, besides non-volatile memory. Hence, the device is functionally equivalent to the combination of an RRAM and a threshold switching selector. It is the potential way to replace the conventional 1S1R structure memory.

Regulatory B Cells Expressing Granzyme B from Tolerant Renal Transplant Patients: Highly Differentiated B Cells with a Unique Pathway with a Specific Regulatory Profile and Strong Interactions with Immune System Cells.

The aim of our study was to determine whether granzyme B-expressing regulatory B cells (GZMB+ B cells) are enriched in the blood of transplant patients with renal graft tolerance. To achieve this goal, we analysed two single-cell RNA sequencing (scRNAseq) datasets: (1) peripheral blood mononuclear cells (PBMCs), including GZMB+ B cells from renal transplant patients, i.e., patients with stable graft function on conventional immunosuppressive treatment (STA, n = 3), drug-free tolerant patients (TOL, n = 3), and patients with antibody-mediated rejection (ABMR, n = 3), and (2) ex-vivo-induced GZMB+ B cells from these groups. In the patient PBMCs, we first showed that natural GZMB+ B cells were enriched in genes specific to Natural Killer (NK) cells (such as NKG7 and KLRD1) and regulatory B cells (such as GZMB, IL10, and CCL4). We performed a pseudotemporal trajectory analysis of natural GZMB+ B cells and showed that they were highly differentiated B cells with a trajectory that is very different from that of conventional memory B cells and linked to the transcription factor KLF13. By specifically analysing GZMB+ natural B cells in TOLs, we found that these cells had a very specific transcriptomic profile associated with a reduction in the expression of HLA molecules, apoptosis, and the inflammatory response (in general) in the blood and that this signature was conserved after ex vivo induction, with the induction of genes associated with migration processes, such as CCR7, CCL3, or CCL4. An analysis of receptor/ligand interactions between these GZMB+/- natural B cells and all of the immune cells present in PBMCs also demonstrated that GZMB+ B cells were the B cells that carried the most ligands and had the most interactions with other immune cells, particularly in tolerant patients. Finally, we showed that these GZMB+ B cells were able to infiltrate the graft under inflammatory conditions, thus suggesting that they can act in locations where immune events occur.

Resetting the Drift of Oxygen Vacancies in Ultrathin HZO Ferroelectric Memories by Electrical Pulse Engineering

Ferroelectric HfO2‐based films incorporated in nonvolatile memory devices offer a low‐energy, high‐speed alternative to conventional memory systems. Oxygen vacancies have been rigorously cited in literature to be pivotal in stabilizing the polar noncentrosymmetric phase responsible for ferroelectricity in HfO2‐based films. Thus, the ability to regulate and control oxygen vacancy migration in operando in such materials would potentially offer step changing new functionalities, tunable electrical properties, and enhanced device lifespan. Herein, a novel in‐ operando approach to control both wake‐up and fatigue device dynamics is reported. Via clever design of short ad hoc square electrical pulses, both wake‐up can be sped up and both fatigue and leakage inside the film can be reduced, key factors for enhancing the performance of memory devices. Using plasmon‐enhanced photoluminescence and dark‐field spectroscopy (sensitive to <1% vacancy variation), evidence that the electrical pulses give rise to oxygen vacancy redistribution is provided and it is shown that pulse engineering effectively delays wake‐up and reduces fatigue characteristics of the HfO2‐based films. Comprehensive analysis also includes impedance spectroscopy measurements, which exclude any influence of polarization reversal or domain wall movement in interpretation of results.

Modeling antigen-specific [formula omitted] cell dynamics following Hepatitis B Vaccination indicates differences between conventional and regulatory [formula omitted] cell dynamics

Our study aims to investigate the dynamics of conventional memory T cells (Tconv) and regulatory memory T cells (Treg) following activation, and to explore potential differences between these two cell types. To achieve this, we developed advanced statistical mixed models based on mathematical models of ordinary differential equations (ODE), which allowed us to transform post-vaccination immunological processes into mathematical formulas. These models were applied to in-house data from a de novo Hepatitis B vaccination trial. By accounting for inter- and intra-individual variability, our models provided good fits for both antigen-specific Tconv and Treg cells, overcoming the challenge of studying these complex processes. Our modeling approach provided a deeper understanding of the immunological processes underlying T cell development after vaccination. Specifically, our analysis revealed several important findings regarding the dynamics of Tconv and Treg cells, as well as their relationship to seropositivity for Herpes Simplex Virus Type 1 (HSV-1) and Epstein-Barr Virus (EBV), and the dynamics of antibody response to vaccination. Firstly, our modeling indicated that Tconv dynamics suggest the existence of two T cell types, in contrast to Treg dynamics where only one T cell type is predicted. Secondly, we found that individuals who converted to a positive antibody response to the vaccine earlier had lower decay rates for both Tregs and Tconv cells, which may have important implications for the development of more effective vaccination strategies. Additionally, our modeling showed that HSV-1 seropositivity negatively influenced Tconv cell expansion after the second vaccination, while EBV seropositivity was associated with higher Treg expansion rates after vaccination. Overall, this study provides a critical foundation for understanding the dynamic processes underlying T cell development after vaccination.

Exhausted Lag-3+ CD4+ T cells are increased in pediatric Inflammatory Bowel Disease.

T cells are one of the main drivers of inflammatory bowel diseases (IBD). Infliximab (IFX) is used in the treatment of IBD as an anti-inflammatory drug to induce remission by neutralizing TNFα. We determined the individual chemokine/homing receptor and cytokine profile in pediatric IBD patients before and during IFX therapy to identify predictive biomarkers for therapy success. Peripheral blood CD4+ cells from pediatric patients with IBD were immunomagnetically isolated and either directly analyzed by FACS for cell distribution and chemokine/homing receptor expression or evaluated for cytokine production after in-vitro-stimulation. 21 responders (RS) and 21 non-responders (NRS) were recruited. Before IFX therapy, flow cytometry revealed decreased percentages of naïve conventional T cells in pediatric IBD patients. The proportions of CD62-L+ T cells were decreased in both CD and UC therapy responders. The cytokine profile of T cells was highly altered in IBD patients compared to healthy controls (HC). During IFX therapy, the frequencies of conventional memory and regulatory memory T cells expanded in both cohorts. IFX response was marked by a decrease of α4β7+ and IFNγ+ memory T cells in both CD and UC. In contrast, frequencies of Lag-3+ T cells proved to be significantly increased in NRS. These observations were irrespective of the underlying disease. T cells of pediatric IBD patients display an activated and rather Th1/Th17 shifted phenotype The increased expression of the checkpoint molecule Lag-3 on T cells of NRS resembles a more exhausted phenotype than in RS and HC which appeared to be a relevant predictive marker for therapy failure.