Hardware implementation of a true random number generator integrating a hexagonal boron nitride memristor with a commercial microcontroller.

The development of the internet-of-things requires cheap, light, small and reliable true random number generator (TRNG) circuits to encrypt the data-generated by objects or humans-before transmitting them. However, all current solutions consume too much power and require a relatively large battery, hindering the integration of TRNG circuits on most objects. Here we fabricated a TRNG circuit by exploiting stable random telegraph noise (RTN) current signals produced by memristors made of two-dimensional (2D) multi-layered hexagonal boron nitride (h-BN) grown by chemical vapor deposition and coupled with inkjet-printed Ag electrodes. When biased at small constant voltages (≤70 mV), the Ag/h-BN/Ag memristors exhibit RTN signals with very low power consumption (∼5.25 nW) and a relatively high current on/off ratio (∼2) for long periods (>1 hour). We constructed TRNG circuits connecting an h-BN memristor to a small, light and cheap commercial microcontroller, producing a highly-stochastic, high-throughput signal (up to 7.8 Mbit s-1) even if the RTN at the input gets interrupted for long times up to 20 s, and if the stochasticity of the RTN signal is reduced. Our study presents the first full hardware implementation of 2D-material-based TRNGs, enabled by the unique stability and figures of merit of the RTN signals in h-BN based memristors.

A Hybrid CMOS-Memristor Spiking Neural Network Supporting Multiple Learning Rules.

Artificial intelligence (AI) is changing the way computing is performed to cope with real-world, ill-defined tasks for which traditional algorithms fail. AI requires significant memory access, thus running into the von Neumann bottleneck when implemented in standard computing platforms. In this respect, low-latency energy-efficient in-memory computing can be achieved by exploiting emerging memristive devices, given their ability to emulate synaptic plasticity, which provides a path to design large-scale brain-inspired spiking neural networks (SNNs). Several plasticity rules have been described in the brain and their coexistence in the same network largely expands the computational capabilities of a given circuit. In this work, starting from the electrical characterization and modeling of the memristor device, we propose a neuro-synaptic architecture that co-integrates in a unique platform with a single type of synaptic device to implement two distinct learning rules, namely, the spike-timing-dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM). This architecture, by exploiting the aforementioned learning rules, successfully addressed two different tasks of unsupervised learning.

Memristive technologies for data storage, computation, encryption, and radio-frequency communication.

Memristive devices, which combine a resistor with memory functions such that voltage pulses can change their resistance (and hence their memory state) in a nonvolatile manner, are beginning to be implemented in integrated circuits for memory applications. However, memristive devices could have applications in many other technologies, such as non-von Neumann in-memory computing in crossbar arrays, random number generation for data security, and radio-frequency switches for mobile communications. Progress toward the integration of memristive devices in commercial solid-state electronic circuits and other potential applications will depend on performance and reliability challenges that still need to be addressed, as described here.

Emergence of associative learning in a neuromorphic inference network.

Objective. In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes-by modelling the activity of functional neural networks at a mesoscopic scale-the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored.Approach.We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller.Main results. Persistent changes of synaptic strength-that mirrored neurophysiological observations-emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures.Significance. These findings show that: (a) an ensemble of free energy minimizing neurons-organized in a biological plausible architecture-can recapitulate functional self-organization observed in nature, such as associative plasticity, and (b) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence.

Standards for the Characterization of Endurance in Resistive Switching Devices.

Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >106 cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.

Multi-Input Logic-in-Memory for Ultra-Low Power Non-Von Neumann Computing.

Logic-in-memory (LIM) circuits based on the material implication logic (IMPLY) and resistive random access memory (RRAM) technologies are a candidate solution for the development of ultra-low power non-von Neumann computing architectures. Such architectures could enable the energy-efficient implementation of hardware accelerators for novel edge computing paradigms such as binarized neural networks (BNNs) which rely on the execution of logic operations. In this work, we present the multi-input IMPLY operation implemented on a recently developed smart IMPLY architecture, SIMPLY, which improves the circuit reliability, reduces energy consumption, and breaks the strict design trade-offs of conventional architectures. We show that the generalization of the typical logic schemes used in LIM circuits to multi-input operations strongly reduces the execution time of complex functions needed for BNNs inference tasks (e.g., the 1-bit Full Addition, XNOR, Popcount). The performance of four different RRAM technologies is compared using circuit simulations leveraging a physics-based RRAM compact model. The proposed solution approaches the performance of its CMOS equivalent while bypassing the von Neumann bottleneck, which gives a huge improvement in bit error rate (by a factor of at least 108) and energy-delay product (projected up to a factor of 1010).

On the Modeling of the Donor/Acceptor Compensation Ratio in Carbon-Doped GaN to Univocally Reproduce Breakdown Voltage and Current Collapse in Lateral GaN Power HEMTs.

The intentional doping of lateral GaN power high electron mobility transistors (HEMTs) with carbon (C) impurities is a common technique to reduce buffer conductivity and increase breakdown voltage. Due to the introduction of trap levels in the GaN bandgap, it is well known that these impurities give rise to dispersion, leading to the so-called "current collapse" as a collateral effect. Moreover, first-principles calculations and experimental evidence point out that C introduces trap levels of both acceptor and donor types. Here, we report on the modeling of the donor/acceptor compensation ratio (CR), that is, the ratio between the density of donors and acceptors associated with C doping, to consistently and univocally reproduce experimental breakdown voltage (VBD) and current-collapse magnitude (ΔICC). By means of calibrated numerical device simulations, we confirm that ΔICC is controlled by the effective trap concentration (i.e., the difference between the acceptor and donor densities), but we show that it is the total trap concentration (i.e., the sum of acceptor and donor densities) that determines VBD, such that a significant CR of at least 50% (depending on the technology) must be assumed to explain both phenomena quantitatively. The results presented in this work contribute to clarifying several previous reports, and are helpful to device engineers interested in modeling C-doped lateral GaN power HEMTs.

Advanced Data Encryption ​using 2D Materials.

Advanced data encryption requires the use of true random number generators (TRNGs) to produce unpredictable sequences of bits. TRNG circuits with high degree of randomness and low power consumption may be fabricated by using the random telegraph noise (RTN) current signals produced by polarized metal/insulator/metal (MIM) devices as entropy source. However, the RTN signals produced by MIM devices made of traditional insulators, i.e., transition metal oxides like HfO2 and Al2 O3 , are not stable enough due to the formation and lateral expansion of defect clusters, resulting in undesired current fluctuations and the disappearance of the RTN effect. Here, the fabrication of highly stable TRNG circuits with low power consumption, high degree of randomness (even for a long string of 224  - 1 bits), and high throughput of 1 Mbit s-1 by using MIM devices made of multilayer hexagonal boron nitride (h-BN) is shown. Their application is also demonstrated to produce one-time passwords, which is ideal for the internet-of-everything. The superior stability of the h-BN-based TRNG is related to the presence of few-atoms-wide defects embedded within the layered and crystalline structure of the h-BN stack, which produces a confinement effect that avoids their lateral expansion and results in stable operation.

Exercise and apulian hypocaloric diet affect adipokine changes and gastric banding-induced weight loss: A prospective study on severe obese subjects.

BACKGROUND: Adiponectin and Resistin correlate with insulin sensitivity and cardiovascular risk, respectively. This study aimed to identify lifestyle factors that modulate changes in Adiponectin and Resistin levels after gastric banding positioning (LapGB). MATERIALS AND METHODS: Before (T0), 3 months (T3), 6 months (T6), and 12 months (T12) after LapGB, serum Adiponectin and Resistin levels were evaluated in a single-centre prospective study including a cohort of 27 non-diabetic obese subjects (S-Ob, BMI ≥35 kg/m2). After surgery, a dietitian checked the adherence of S-Ob to an Apulian hypocaloric diet (aphypoD)/physical activity (phA) and, according to their high or low compliance to aphypoD/phA, S-Ob were included in group 1 (n = 14) or 2 (n = 13) respectively. Serum Adiponectin and Resistin were also measured in 10 healthy controls. RESULTS: At baseline, Resistin levels were significantly higher and Adiponectin levels significantly lower in S-Ob than in controls. After surgery, group 1 showed a 50.2% excess weight loss (%EWL), significantly decreased Resistin levels at T12 and increased Adiponectin levels at both T6 and T12 as compared with baseline. Group 2 showed 24.6 %EWL at T12, decreased Adiponectin levels at T6 and T12 as compared with baseline, but unaltered Resistin levels. After surgery, group 1 followed aphypoD/phA, while group 2 did not. CONCLUSIONS: LapGB fails to improve cardiovascular risk markers (Resistin) in S-Ob not improving lifestyle. Future studies might investigate these findings in a larger cohort and display whether aphypoD is more effective than other dietary intervention on cardiovascular risk in subjects undergoing LapGB or other Bariatric procedures.

Electroforming in Metal-Oxide Memristive Synapses.

Memristors have shown an extraordinary potential to emulate the plastic and dynamic electrical behaviors of biological synapses and have been already used to construct neuromorphic systems with in-memory computing and unsupervised learning capabilities; moreover, the small size and simple fabrication process of memristors make them ideal candidates for ultradense configurations. So far, the properties of memristive electronic synapses (i.e., potentiation/depression, relaxation, linearity) have been extensively analyzed by several groups. However, the dynamics of electroforming in memristive devices, which defines the position, size, shape, and chemical composition of the conductive nanofilaments across the device, has not been analyzed in depth. By applying ramped voltage stress (RVS), constant voltage stress (CVS), and pulsed voltage stress (PVS), we found that electroforming is highly affected by the biasing methods applied. We also found that the technique used to deposit the oxide, the chemical composition of the adjacent metal electrodes, and the polarity of the electrical stimuli applied have important effects on the dynamics of the electroforming process and in subsequent post-electroforming bipolar resistive switching. This work should be of interest to designers of memristive neuromorphic systems and could open the door for the implementation of new bioinspired functionalities into memristive neuromorphic systems.

Multiscale Modeling for Application-Oriented Optimization of Resistive Random-Access Memory.

Memristor-based neuromorphic systems have been proposed as a promising alternative to von Neumann computing architectures, which are currently challenged by the ever-increasing computational power required by modern artificial intelligence (AI) algorithms. The design and optimization of memristive devices for specific AI applications is thus of paramount importance, but still extremely complex, as many different physical mechanisms and their interactions have to be accounted for, which are, in many cases, not fully understood. The high complexity of the physical mechanisms involved and their partial comprehension are currently hampering the development of memristive devices and preventing their optimization. In this work, we tackle the application-oriented optimization of Resistive Random-Access Memory (RRAM) devices using a multiscale modeling platform. The considered platform includes all the involved physical mechanisms (i.e., charge transport and trapping, and ion generation, diffusion, and recombination) and accounts for the 3D electric and temperature field in the device. Thanks to its multiscale nature, the modeling platform allows RRAM devices to be simulated and the microscopic physical mechanisms involved to be investigated, the device performance to be connected to the material's microscopic properties and geometries, the device electrical characteristics to be predicted, the effect of the forming conditions (i.e., temperature, compliance current, and voltage stress) on the device's performance and variability to be evaluated, the analog resistance switching to be optimized, and the device's reliability and failure causes to be investigated. The discussion of the presented simulation results provides useful insights for supporting the application-oriented optimization of RRAM technology according to specific AI applications, for the implementation of either non-volatile memories, deep neural networks, or spiking neural networks.

Understanding Current Instabilities in Conductive Atomic Force Microscopy.

: Conductive atomic force microscopy (CAFM) is one of the most powerful techniques in studying the electrical properties of various materials at the nanoscale. However, understanding current fluctuations within one study (due to degradation of the probe tips) and from one study to another (due to the use of probe tips with different characteristics), are still two major problems that may drive CAFM researchers to extract wrong conclusions. In this manuscript, these two issues are statistically analyzed by collecting experimental CAFM data and processing them using two different computational models. Our study indicates that: (i) before their complete degradation, CAFM tips show a stable state with degraded conductance, which is difficult to detect and it requires CAFM tip conductivity characterization before and after the CAFM experiments; and (ii) CAFM tips with low spring constants may unavoidably lead to the presence of a ~1.2 nm thick water film at the tip/sample junction, even if the maximum contact force allowed by the setup is applied. These two phenomena can easily drive CAFM users to overestimate the properties of the samples under test (e.g., oxide thickness). Our study can help researchers to better understand the current shifts that were observed during their CAFM experiments, as well as which probe tip to use and how it degrades. Ultimately, this work may contribute to enhancing the reliability of CAFM investigations.

New Insights on Water Buffalo Genomic Diversity and Post-Domestication Migration Routes From Medium Density SNP Chip Data.

The domestic water buffalo is native to the Asian continent but through historical migrations and recent importations, nowadays has a worldwide distribution. The two types of water buffalo, i.e., river and swamp, display distinct morphological and behavioral traits, different karyotypes and also have different purposes and geographical distributions. River buffaloes from Pakistan, Iran, Turkey, Egypt, Romania, Bulgaria, Italy, Mozambique, Brazil and Colombia, and swamp buffaloes from China, Thailand, Philippines, Indonesia and Brazil were genotyped with a species-specific medium-density 90K SNP panel. We estimated the levels of molecular diversity and described population structure, which revealed historical relationships between populations and migration events. Three distinct gene pools were identified in pure river as well as in pure swamp buffalo populations. Genomic admixture was seen in the Philippines and in Brazil, resulting from importations of animals for breed improvement. Our results were largely consistent with previous archeological, historical and molecular-based evidence for two independent domestication events for river- and swamp-type buffaloes, which occurred in the Indo-Pakistani region and close to the China/Indochina border, respectively. Based on a geographical analysis of the distribution of diversity, our evidence also indicated that the water buffalo spread out of the domestication centers followed two major divergent migration directions: river buffaloes migrated west from the Indian sub-continent while swamp buffaloes migrated from northern Indochina via an east-south-eastern route. These data suggest that the current distribution of water buffalo diversity has been shaped by the combined effects of multiple migration events occurred at different stages of the post-domestication history of the species.

The p66Shc protein controls redox signaling and oxidation-dependent DNA damage in human liver cells.

The p66Shc protein mediates oxidative stress-related injury in multiple tissues. Steatohepatitis is characterized by enhanced oxidative stress-mediated cell damage. The role of p66Shc in redox signaling was investigated in human liver cells and alcoholic steatohepatitis. HepG2 cells with overexpression of wild-type or mutant p66Shc, with Ser36 replacement by Ala, were obtained through infection with recombinant adenoviruses. Reactive oxygen species and oxidation-dependent DNA damage were assessed by measuring dihydroethidium oxidation and 8-hydroxy-2'-deoxyguanosine accumulation into DNA, respectively. mRNA and protein levels of signaling intermediates were evaluated in HepG2 cells and liver biopsies from control and alcoholic steatohepatitis subjects. Exposure to H2O2 increased reactive oxygen species and phosphorylation of p66Shc on Ser36 in HepG2 cells. Overexpression of p66Shc promoted reactive oxygen species synthesis and oxidation-dependent DNA damage, which were further enhanced by H2O2. p66Shc activation also resulted in increased Erk-1/2, Akt, and FoxO3a phosphorylation. Blocking of Erk-1/2 activation inhibited p66Shc phosphorylation on Ser36. Increased p66Shc expression was associated with reduced mRNA levels of antioxidant molecules, such as NF-E2-related factor 2 and its target genes. In contrast, overexpression of the phosphorylation defective p66Shc Ala36 mutant inhibited p66Shc signaling, enhanced antioxidant genes, and suppressed reactive oxygen species and oxidation-dependent DNA damage. Increased p66Shc protein levels and Akt phosphorylation were observed in liver biopsies from alcoholic steatohepatitis compared with control subjects. In human alcoholic steatohepatitis, increased hepatocyte p66Shc protein levels may enhance susceptibility to DNA damage by oxidative stress by promoting reactive oxygen species synthesis and repressing antioxidant pathways.

Does the intragastric balloon have a predictive role in subsequent LAP-BAND(®) surgery? Italian multicenter study results at 5-year follow-up.

BACKGROUND: The intragastric balloon has been reported to be a safe and effective tool for temporary weight loss. The aim of this study is the evaluation of the possible predictive role of intragastric balloon when used before laparoscopic adjustable gastric banding. METHODS: A longitudinal multicenter study was conducted in patients with body mass index (BMI)>35 kg/m(2) who underwent gastric banding with the BioEnterics Intragastric Balloon (BIB). After balloon removal (6 mo), patients were allocated into 2 group according to their percentage of excess weight loss (%EWL): group>25 (%EWL>25%) and group<25 (%EWL<25%). Patients from both group underwent laparoscopic adjustable gastric banding (LAGB) 1-3 months after BIB removal. The LAP-BAND AP band was placed in all patients via pars flaccida. Weight loss parameters were considered in both groups. RESULTS: From January 2005 to December 2009, 1357 patients were enrolled in this study. Mean BMI at time of BIB positioning was 44.9±8.4 (range 29-82.5). After 6 months, at time of removal, mean BMI was 39.4±7.3. According to the cutoff, patients were allocated into group A (n = 699) and group B (n = 658). At this time the mean BMI was 36.4±6.4 and 42.7±6.9 (P = .001) in groups A and B, respectively. At 1-year follow-up from LAGB, mean BMI was 35.8±6.5 and 40.0±7.4 (P<.001) in groups A and B, respectively. This significant difference was confirmed at 3- and 5-year follow-ups. A similar pattern was observed with the %EWL. CONCLUSION: Satisfactory results with BIB are predictive of a positive outcome of LASB at 1, 3, and 5 years after the procedure, and poor results do not inevitably indicate a negative outcome for gastric banding.

Laparoscopic repair of Morgagni hernia in an adult: use of a porcine small intestine submucosa biocompatible prosthesis.

Morgagni hernia is a rare cause of diaphragmatic hernia. There are few reports of laparoscopic repair in the literature. Tension-free hernia closure with synthetic mesh reduces recurrence but occasionally results in complications, such as visceral stricture, erosion or perforation. We report a case of successful laparoscopic repair of a very large symptomatic Morgagni hernia in a 78-year-old patient, treated by positioning a gradually resorbable mesh made from porcine SIS (Surgisis Soft Tissue Graft device--Cook Inc, Bloomington, Ind). After surgery, the patient reported an immediate, marked improvement in clinical symptoms. During the postoperative course, pleural and pericardial leakage occurred. The pleural leakage was immediately drained with thoracentesis, and the pericardial leakage was treated conservatively. No other complications occurred after patient discharge. One year later, the patient was in very good general condition. No recurrence was documented with abdominal CT scan, which also demonstrated connective tissue proliferation that was progressively replacing the prosthesis. Laparoscopic repair is a safe, efficacious procedure for the treatment of diaphragmatic hernia, and presents all the advantages of minimally invasive surgery. The use of new types of material featuring marked biocompatibility and gradual reabsorbability characteristics offers considerable benefits.

Mucinous cystic neoplasm of the pancreas: a case report.

Cystic neoplasms of the pancreas account for only a small percentage of pancreatic tumours. They include mucinous cystic tumours, which have a higher incidence in females in their forties or fifties. Cystic neoplasms of the pancreas can present in a benign, borderline or malignant form. These tumours have a natural evolution from a benign (mucinous cystadenoma) to a malignant form (cystadenocarcinoma). It is not always easy to diagnose cystic tumours, including mucinous cystic tumours of the pancreas, and the final diagnosis is often reached only after the surgical procedure, which is the gold standard treatment of this disease. We present the case of a 56-year-old woman affected by a mucinous cystic tumour of the body-tail of the pancreas, who underwent distal splenopancreasectomy. She was discharged on postoperative day 12. After an 18-month followup, she is in good general condition and disease-free.

Laparoscopic treatment of simultaneous splenic lymphangioma and non-Hodgkin's lymphoma: report of a case.

We report, to the best of our knowledge, the first case of simultaneous splenic lymphangioma and non-Hodgkin's B-cell lymphoma with liver and bone marrow involvement arising in a 69-year-old woman suffering from chronic hepatitis C infection treated with laparoscopic splenectomy followed by polychemotherapy. After 22 months from surgical treatment, the patient is alive without signs of residual disease. According to our experience, laparoscopic splenectomy followed by polychemotherapy seems to be an effective treatment for simultaneous splenic lymphangioma and non-Hodgkin's B-cell lymphoma.

Expression and secretion of the atrial natriuretic peptide in human adipose tissue and preadipocytes.

OBJECTIVE: Atrial natriuretic peptide (ANP) is a secretory hormone displaying diuretic, natriuretic, and vasorelaxant activities. Recently, its lipolytic activity has been reported. Since the expression of ANP in adipose tissue has not been documented, we used real-time reverse transcriptase polymerase chain reaction (RT-PCR) to investigate the expression of ANP in human adipose tissue and preadipocytes. RESEARCH METHODS AND PROCEDURES: RNA was extracted from the human adipose tissue of severely obese premenopausal women as well as from human preadipocytes. For human preadipocytes, two cell systems were investigated: the human preadipose immortalized (Chub-S7) cells, a well-characterized human preadipose cell line, and primary preadipocytes derived from the stromal vascular fraction of the human adipose tissue. We measured the mRNA of ANP, of corin (a transmembrane serine protease involved in the conversion of pro-ANP to ANP) and of uncoupling protein 2 (UCP2; a control gene known to be ubiquitously expressed). The expression of ANP was also investigated using immunofluorescence and radioimmunoassay in Chub-S7 cells and human primary preadipocytes in culture. RESULTS: Our results indicate that ANP and corin are expressed at the mRNA level in human adipose tissue and preadipocytes. Immunofluorescence experiments demonstrated that pro-ANP was expressed in Chub-S7 cells. In addition, ANP secretion could be measured in Chub-S7 cells and human primary preadipocytes in culture. Rosiglitazone, a selective peroxisome proliferator-activated receptor type gamma (PPAR-gamma) agonist promoting adipocyte differentiation, was found to modulate both ANP expression and secretion in preadipocytes. DISCUSSION: Our findings suggest the existence of an autocrine/paracrine system for ANP in the human adipose tissue whose implications in lipolysis and cardiovascular function need to be further explored.