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Abstract The tripartite ATP-independent periplasmic (TRAP) transporters enable Vibrio cholerae and Haemophilus influenzae to acquire sialic acid, aiding their colonization of human hosts. This process depends on SiaP, a substrate-binding protein (SBP) that captures and delivers sialic acid to the transporter. We identified 11 nanobodies that bind specifically to the SiaP proteins from H. influenzae (HiSiaP) and V. cholerae (VcSiaP). Two nanobodies inhibited sialic acid binding. Detailed structural and biophysical studies of one nanobody-SBP complex revealed an allosteric inhibition mechanism, preventing ligand binding and releasing pre-bound sialic acid. A hydrophobic surface pocket of the SBP is crucial for the allosteric mechanism and for the conformational rearrangement that occurs upon binding of sialic acid to the SBP. Our findings provide new clues regarding the mechanism of TRAP transporters, as well as potential starting points for novel drug design approaches to starve these human pathogens of important host-derived molecules.

Abstract Resistive Random Access Memory (RRAM) has gained considerable momentum due to its non-volatility and energy efficiency. Material and device scientists have been proposing novel material stacks that can mimic the “ideal memristor” which can deliver performance, energy efficiency, reliability and accuracy. However, designing RRAM-based systems is challenging. Engineering a new material stack, designing a device, and experimenting takes significant time for material and device researchers. Furthermore, the acceptability of the device is ultimately decided at the system level. We see a gap here where there is a need for facilitating material and device researchers with a “push button” modeling framework that allows to evaluate the efficacy of the device at system level during early device design stages. Speed, accuracy, and adaptability are the fundamental requirements of this modelling framework. In this paper, we propose a digital twin (DT)-like modeling framework that automatically creates RRAM device models from device measurement data. Furthermore, the model incorporates the peripheral circuit to ensure accurate energy and performance evaluations. We demonstrate the DT generation and DT usage for multiple RRAM technologies and applications and illustrate the achieved performance of our GPU implementation. We conclude with the application of our modeling approach to measurement data from two distinct fabricated devices, validating its effectiveness in a neural network processing an Electrocardiogram (ECG) dataset and incorporating Fault Aware Training (FAT).

Abstract Self-rectifying memristive devices have emerged as promising contenders for low-power in-memory computing, presenting numerous advantages. However, characterizing the functional behavior of passive crossbar arrays incorporating these devices remains challenging due to sophisticated parasitic currents stemming from rich memristive dynamic behavior. Conventional methods using read margin assessments to evaluate functional behavior in passive crossbars are hindered by the voltage divider effect from the pull-up resistor. In this study, we propose a novel performance metric, $$\Delta$$ Δ SC, harnessing sneak path currents to assess functional behavior. Through the application of a pair of negative rectification factors, $$\text {RF}_\text {n, L}$$ RF n, L and $$\text {RF}_\text {n, H}$$ RF n, H , we comprehensively delineate dynamic rectification behavior in both positive and negative bias regimes, as well as in low-resistance state and high-resistance state, deviating from conventional metrics such as on/off ratios, nonlinearity, and rectifying factors. Notably, $$\Delta$$ Δ SC provides a quantitative evaluation of the interaction between sneak path currents and read margin, demonstrating its efficacy and addressing a pivotal research gap in the field. For instance, employing self-rectifying BiFeO $$_3$$ 3 memristive cells featuring $$\text {RF}_\text {n, L}$$ RF n, L = 1.22E3 and $$\text {RF}_\text {n, H}$$ RF n, H = 9.27, we showcase the successful functional performance of a passive crossbar array, achieving $$\Delta$$ Δ SC < 2.19E−2, while ensuring a read margin > 0.

Abstract Memristive devices based on the valence change mechanism are highly interesting candidates for data storage and hardware implementation of synapses in neuromorphic circuits. Although long‐term retention is often required for data storage applications, a slight resistance drift of the low resistive state (LRS) is observed even for stable devices. For other devices, the LRS has been observed to decay rapidly to the high resistive state (HRS). These types of devices are of interest for neuromorphic circuits where short‐term plasticity is required. In this work, the LRS relaxation of volatile, crystalline Pt/SrTiO3/Nb:SrTiO3: devices is investigated in detail, yielding time constants ranging from milliseconds to seconds. The decay is analyzed in terms of the Gibbs free energy gradient for the contribution of oxygen ion migration. A relaxation model based on drift‐diffusion dynamics is presented. The model may serve as a tool for developing guidelines and design rules for future volatile memristive technology based on Schottky barrier mediated electron transport.

Immune exhaustion is a hallmark of ovarian cancer. Using multiparametric flow cytometry, the study aimed to analyze protein expression of novel immunological targets on CD3+ T cells isolated from the peripheral blood (n = 20), malignant ascites (n = 16), and tumor tissue (n = 6) of patients with ovarian cancer (OVCA). The study revealed an increased proportion of effector memory CD8+ T cells in OVCA tissue and malignant ascites. An OVCA-characteristic PD-1high CD8+ T cell population was detected, which differed from PD-1lowCD8+ T cells by increased co-expression of TIGIT, CD39, and HLA-DR. In addition, these OVCA-characteristic CD8+ T cells showed reduced expression of the transcription factor TCF-1, which may also indicate reduced effector function and memory formation. On the contrary, the transcription factor TOX, which significantly regulates terminal T cell-exhaustion, was found more frequently in these cells. Further protein and gene analysis showed that CD39 and CD73 were also expressed on OVCA tumor cells isolated from solid tumors (n = 14) and malignant ascites (n = 9). In the latter compartment, CD39 and CD73 were also associated with the expression of the “don’t eat me” molecule CD24 on tumor cells. Additionally, ascites-derived CD24+EpCAM+ tumor cells showed a higher frequency of CD39+ or CD73+ cells. Furthermore, CD39 expression was associated with unfavorable clinical parameters. Expression of CD39 on T cells was upregulated through CD3/CD28 stimulation and its blockade by a newly developed nanobody construct resulted in increased proliferation (eFluor), activation (CD25 and CD134), and production of cytotoxic cytokines (IFN-γ, TNF-α, and granzyme-B) of CD8+ T cells.

Abstract Due to their exceptional solubility and stability, nanobodies have emerged as powerful building blocks for research tools and therapeutics. However, their generation in llamas is cumbersome and costly. Here, by inserting an engineered llama immunoglobulin heavy chain (IgH) locus into IgH-deficient mice, we generate a transgenic mouse line, which we refer to as ‘LamaMouse’. We demonstrate that LamaMice solely express llama IgH molecules without association to Igκ or λ light chains. Immunization of LamaMice with AAV8, the receptor-binding domain of the SARS-CoV-2 spike protein, IgE, IgG2c, and CLEC9A enabled us to readily select respective target-specific nanobodies using classical hybridoma and phage display technologies, single B cell screening, and direct cloning of the nanobody-repertoire into a mammalian expression vector. Our work shows that the LamaMouse represents a flexible and broadly applicable platform for a facilitated selection of target-specific nanobodies.

CD39 is the major enzyme controlling the levels of extracellular adenosine triphosphate (ATP) via the stepwise hydrolysis of ATP to adenosine diphosphate (ADP) and adenosine monophosphate (AMP). As extracellular ATP is a strong promoter of inflammation, monoclonal antibodies (mAbs) blocking CD39 are utilized therapeutically in the field of immune-oncology. Though anti-CD39 mAbs are highly specific for their target, they lack deep penetration into the dense tissue of solid tumors, due to their large size. To overcome this limitation, we generated and characterized nanobodies that targeted and blocked human CD39. From cDNA-immunized alpacas we selected 16 clones from seven nanobody families that bind to two distinct epitopes of human CD39. Among these, clone SB24 inhibited the enzymatic activity of CD39. Of note, SB24 blocked ATP degradation by both soluble and cell surface CD39 as a 15kD monomeric nanobody. Dimerization via fusion to an immunoglobulin Fc portion further increased the blocking potency of SB24 on CD39-transfected HEK cells. Finally, we confirmed the CD39 blocking properties of SB24 on human PBMCs. In summary, SB24 provides a new small biological antagonist of human CD39 with potential application in cancer therapy.

Computing‐in‐memory (CIM) is a promising approach for overcoming the memory‐wall problem in conventional von‐Neumann architectures. This is done by performing certain computation tasks directly in the storage subsystem without transferring data between storage and processing units. Stateful and non‐stateful CIM concepts are recently attracting lots of interest, which are demonstrated as logical complete, energy efficient, and compatible with dense crossbar structures. However, sneak‐path currents in passive resistive random access memory (RRAM) crossbars degrade the operation reliability and require the usage of active 1 Transistor–1 Resistance (1T‐1R) bitcell designs. In this article, the arithmetic performance and reliability are investigated based on experimental measurements and variability‐aware circuit simulations. Herein, it is aimed for the evaluation of logic operations specifically with fully integrated 1T–1R crossbar devices. Based on these operations, an N‐bit full adder with optimized energy consumption and latency is demonstrated by combining stateful and non‐stateful CIM logic styles with regard to the specific conditions in active 1T–1R RRAM crossbars.

Abstract Memristive devices with valence change mechanism (VCM) show promise for neuromorphic data processing, although emulation of synaptic behavior with analog weight updates remains a challenge. Standard filamentary and area‐dependent resistive switching exhibit characteristic differences in the transition from the high to low resistance state, which is either abrupt with inherently high variability or gradual and allows quasi‐analog operation. In this study, the two switching modes are clearly correlated to differences in the microstructure and electronic structure for Pt/Al2O3/TiOx/Cr/Pt devices made from amorphous layers of 1.2 nm Al2O3 and 7 nm TiOx by atomic layer deposition. For the filamentary mode, operando spectromicroscopy experiments identify a localized region of ≈50 nm in diameter of reduced titania surrounded by crystalline rutile‐like TiO2, highlighting the importance of Joule heating for this mode. In contrast, both oxide layers remain in their amorphous state for the interfacial mode, which proves that device temperature during switching stays below 670 K, which is the TiO2 crystallization temperature. The analysis of the electronic conduction behavior confirms that the interfacial switching occurs by modulating the effective tunnel barrier width due to accumulation and depletion of oxygen vacancies at the Al2O3/TiOx interface. The results are transferable to other bilayer stacks.

Resistive switching devices are candidates to be used as weight for the future hardware realization of neuronal networks. Herein, these resistive switches base on oxygen vacancy migration and can be toggled between different resistance states. Internal processes lead to a variability over time in these states. To understand this, a dynamical model is developed, which links the internal physical mechanism to the current response. Thereby, the electronic current is calculated by tunneling processes over oxygen vacancies. The model allows to understand the physical transport properties and the current–voltage dependence. Integrating ionic hopping processes clarifies the variability in the electronic conduction. The derived model does not require empirical input parameters and only depends on the properties of the materials. This allows to use literature values and independently compare them to electrical measurements. The model predicts the experiments correctly. Thereupon, the physical properties of the conduction are investigated with respect to the transport over discrete energy levels, which change under the hopping of oxygen vacancies. Afterwards, the current level and variability are simulated for increasing oxygen vacancy concentrations to investigate analog switching, which is required for neuronal network layers. Finally, the variability is tested with multiple interacting cells in a neuronal layer.

Analog compute schemes and compute-in-memory (CIM) have emerged in an effort to reduce the increasing power hunger of convolutional neural networks (CNNs), which exceeds the constraints of edge devices. Memristive device types are a relatively new offering with interesting opportunities for unexplored circuit concepts. In this work, the use of memristive devices in cascaded time-domain CIM (TDCIM) is introduced with the primary goal of reducing the size of fully unrolled architectures. The different effects influencing the determinism in memristive devices are outlined together with reliability concerns. Architectures for binary as well as multibit multiply and accumulate (MAC) cells are presented and evaluated. As more involved circuits offer more accurate compute result, a tradeoff between design effort and accuracy comes into the picture. To further evaluate this tradeoff, the impact of variations on overall compute accuracy is discussed. The presented cells reach an energy/OP of 0.23 fJ at a size of $1.2~{\mu{ }}\text{m}^{2}$ for binary and 6.04 fJ at $3.2~\mu \text{m}^{2}$ for $4\times 4$ bit MAC operations.

The proliferation of machine learning algorithms in everyday applications such as image recognition or language translation has increased the pressure to adapt underlying computing architectures towards these algorithms. Application specific integrated circuits (ASICs) such as the Tensor Processing Units by Google, Hanguang by Alibaba or Inferentia by Amazon Web Services were designed specifically for machine learning algorithms and have been able to outperform CPU based solutions by great margins during training and inference. As newer generations of chips allow handling of and computation on more and more data, the size of neural networks has dramatically increased, while the challenges they are trying to solve have become more complex. Neuromorphic computing tries to take inspiration from biological information processing systems, aiming to further improve the efficiency with which these networks can be trained or the inference can be performed. Enhancing neuromorphic computing architectures with memristive devices as non-volatile storage elements could potentially allow for even higher energy efficiencies. Their ability to mimic synaptic plasticity dynamics brings neuromorphic architectures closer to the biological role models. So far, memristive devices are mainly investigated for the emulation of the weights of neural networks during training and inference as their non-volatility would enable both processes in the same location without data transfer. In this paper, we explore realisations of different synapses build from memristive ReRAM devices, based on the Valence Change Mechanism. These synapses are the 1R synapse, the NR synapse and the 1T1R synapse. For the 1R synapse, we propose three dynamical regimes and explore their performance through different synapse criteria. For the NR synapse, we discuss how the same dynamical regimes can be addressed in a more reliable way. We also show experimental results measured on ZrOx devices to support our simulation based claims. For the 1T1R synapse, we explore the trade offs between the connection direction of the ReRAM device and the transistor. For all three synapse concepts we discuss the impact of device-to-device and cycle-to-cycle variability. Additionally, the impact of the stimulation mode on the observed behavior is discussed.

Abstract Background: Hamburg is a city state of approximately 1.9 Mio inhabitants in Northern Germany. Currently, the COVID‐19 epidemic that had largely subsided during last summer is resurging in Hamburg and in other parts of the world, underlining the need for additional tools to monitor SARS‐CoV‐2 antibody responses. Aim: We aimed to develop and validate a simple, low‐cost assay for detecting antibodies against the native coronavirus 2 spike protein (CoV‐2 S) that does not require recombinant protein or virus. Method: We transiently co‐transfected HEK cells or CHO cells with expression vectors encoding CoV‐2 S and nuclear GFP. Spike protein‐specific antibodies in human serum samples bound to transfected cells were detected with fluorochrome conjugated secondary antibodies by flow cytometry orimmunofluorescence microscopy. We applied this assay to monitor antibody development in COVID‐19 patients, household contacts, and hospital personnel during the ongoing epidemic in the city state of Hamburg. Results: All recovered COVID‐19 patients showed high levels of CoV‐2 S‐specific antibodies. With one exception, all household members that did not develop symptoms also did not develop detectable antibodies. Similarly, lab personnel that worked during the epidemic and followed social distancing guidelines remained antibody‐negative. Conclusion: We conclude that high‐titer CoV‐2 S‐specific antibodies are found in most recovered COVID‐19 patients and in symptomatic contacts, but only rarely in asymptomatic contacts. The assay may help health care providers to monitor disease progression and antibody responses in vaccination trials, to identify health care personnel that likely are resistant to re‐infection, and recovered individuals with high antibody titers that may be suitable asplasma and/or antibody donors.

Adenosine triphosphate (ATP) represents a danger signal that accumulates in injured tissues, in inflammatory sites, and in the tumor microenvironment. ATP promotes tumor growth but also anti-tumor immune responses notably via the P2X7 receptor. ATP can also be catabolized by CD39 and CD73 ecto-enzymes into immunosuppressive adenosine. P2X7, CD39 and CD73 have attracted much interest in cancer as targets offering the potential to unleash anti-tumor immune responses. These membrane proteins represent novel purinergic checkpoints that can be targeted by small drugs or biologics. Here, we investigated nanobody-based biologics targeting mainly P2X7, but also CD73, alone or in combination therapies. Blocking P2X7 inhibited tumor growth and improved survival of mice in cancer models that express P2X7. P2X7-potentiation by a nanobody-based biologic was not effective alone to control tumor growth but enhanced tumor control and immune responses when used in combination with oxaliplatin chemotherapy. We also evaluated a bi-specific nanobody-based biologic that targets PD-L1 and CD73. This novel nanobody-based biologic exerted a potent anti-tumor effect, promoting tumor rejection and improving survival of mice in two tumor models. Hence, this study highlights the importance of purinergic checkpoints in tumor control and open new avenues for nanobody-based biologics that may be further exploited in the treatment of cancer.