Quantum gate algorithm for reference-guided DNA sequence alignment

Reference-guided DNA sequencing and alignment is an important process in computational molecular biology. The amount of DNA data grows very fast, and many new genomes are waiting to be sequenced while millions of private genomes need to be re-sequenced. Each human genome has 3.2B base pairs, and each one could be stored with 2 bits of information, so one human genome would take 6.4B bits or ∼760MB of storage (National Institute of General Medical Sciences, n.d.). Today's most powerful tensor processing units cannot handle the volume of DNA data necessitating a major leap in computing power. It is, therefore, important to investigate the usefulness of quantum computers in genomic data analysis, especially in DNA sequence alignment. Quantum computers are expected to be involved in DNA sequencing, initially as parts of classical systems, acting as quantum accelerators. The number of available qubits is increasing annually, and future quantum computers could conduct DNA sequencing, taking the place of classical computing systems. We present a novel quantum algorithm for reference-guided DNA sequence alignment modeled with gate-based quantum computing. The algorithm is scalable, can be integrated into existing classical DNA sequencing systems and is intentionally structured to limit computational errors. The quantum algorithm has been tested using the quantum processing units and simulators provided by IBM Quantum, and its correctness has been confirmed.

Quantum algorithm for de novo DNA sequence assembly based on quantum walks on graphs

De novo DNA sequence assembly is based on finding paths in overlap graphs, which is a NP-hard problem. We developed a quantum algorithm for de novo assembly based on quantum walks in graphs. The overlap graph is partitioned repeatedly to smaller graphs that form a hierarchical structure. We use quantum walks to find paths in low rank graphs and a quantum algorithm that finds Hamiltonian paths in high hierarchical rank. We tested the partitioning quantum algorithm, as well as the quantum algorithm that finds Hamiltonian paths in high hierarchical rank and confirmed its correct operation using Qiskit. We developed a custom simulation for quantum walks to search for paths in low rank graphs. The approach described in this paper may serve as a basis for the development of efficient quantum algorithms that solve the de novo DNA assembly problem.

In-Memory-Computing Realization with a Photodiode/Memristor Based Vision Sensor

State-of-the-art IoT technologies request novel design solutions in edge computing, resulting in even more portable and energy-efficient hardware for in-the-field processing tasks. Vision sensors, processors, and hardware accelerators are among the most demanding IoT applications. Resistance switching (RS) two-terminal devices are suitable for resistive RAMs (RRAM), a promising technology to realize storage class memories. Furthermore, due to their memristive nature, RRAMs are appropriate candidates for in-memory computing architectures. Recently, we demonstrated a CMOS compatible silicon nitride (SiNx) MIS RS device with memristive properties. In this paper, a report on a new photodiode-based vision sensor architecture with in-memory computing capability, relying on memristive device, is disclosed. In this context, the resistance switching dynamics of our memristive device were measured and a data-fitted behavioral model was extracted. SPICE simulations were made highlighting the in-memory computing capabilities of the proposed photodiode-one memristor pixel vision sensor. Finally, an integration and manufacturing perspective was discussed.

Neurocognitive and Neurophysiological Functions Related to ACL Injury: A Framework for Neurocognitive Approaches in Rehabilitation and Return-to-Sports Tests

Context:

Only 55% of the athletes return to competitive sports after an anterior cruciate ligament (ACL) injury. Athletes younger than 25 years who return to sports have a second injury rate of 23%. There may be a mismatch between rehabilitation contents and the demands an athlete faces after returning to sports. Current return-to-sports (RTS) tests utilize closed and predictable motor skills; however, demands on the field are different. Neurocognitive functions are essential to manage dynamic sport situations and may fluctuate after peripheral injuries. Most RTS and rehabilitation paradigms appear to lack this aspect, which might be linked to increased risk of second injury.

Objective:

This systematic and scoping review aims to map existing evidence about neurocognitive and neurophysiological functions in athletes, which could be linked to ACL injury in an integrated fashion and bring an extensive perspective to assessment and rehabilitation approaches.

Data Sources:

PubMed and Cochrane databases were searched to identify relevant studies published between 2005 and 2020 using the keywords ACL, brain, cortical, neuroplasticity, cognitive, cognition, neurocognition, and athletes.

Study Selection:

Studies investigating either neurocognitive or neurophysiological functions in athletes and linking these to ACL injury regardless of their design and technique were included.

Study Design:

Systematic review.

Level of Evidence:

Level 3.

Data Extraction:

The demographic, temporal, neurological, and behavioral data revealing possible injury-related aspects were extracted and summarized.

Results:

A total of 16 studies were included in this review. Deficits in different neurocognitive domains and changes in neurophysiological functions could be a predisposing risk factor for, or a consequence caused by, ACL injuries.

Conclusion:

Clinicians should view ACL injuries not only as a musculoskeletal but also as a neural lesion with neurocognitive and neurophysiological aspects. Rehabilitation and RTS paradigms should consider these changes for assessment and interventions after injury.

Impact of Line Edge Roughness on ReRAM Uniformity and Scaling

We investigate the effects of Line Edge Roughness (LER) of electrode lines on the uniformity of Resistive Random Access Memory (ReRAM) device areas in cross-point architectures. To this end, a modeling approach is implemented based on the generation of 2D cross-point patterns with predefined and controlled LER and pattern parameters. The aim is to evaluate the significance of LER in the variability of device areas and their performances and to pinpoint the most critical parameters and conditions. It is found that conventional LER parameters may induce >10% area variability depending on pattern dimensions and cross edge/line correlations. Increased edge correlations in lines such as those that appeared in Double Patterning and Directed Self-assembly Lithography techniques lead to reduced area variability. Finally, a theoretical formula is derived to explain the numerical dependencies of the modeling method.

Graphene-Modified Interface Controls Transition from VCM to ECM Switching Modes in Ta/TaOx Based Memristive Devices

By modification of the electrode-solid-electrolyte interface with graphene, transit from valence change memories (VCM) to electrochemical metallization memories (ECM) in the cell Ta(C)/Ta2 O5 /Pt is demonstrated, thus, bridging both mechanisms. The ECM operation is discussed in the light of Ta-cation mobility in TaOx . The crucial role of electrochemical processes and moisture in the resistive switching process is also highlighted.

Inert ambient annealing effect on MANOS capacitor memory characteristics

In this work we report on the influence of nitrogen ambient thermal effects on the performance of Pt/Al2O3/Si3N4/SiO2/Si memory capacitors. Two post deposition annealing (PDA) furnace steps were employed, at 850 and 1050 °C both for 15 min. The alumina films were deposited by atomic layer deposition using TMA/H2O at 250 °C. The structural characteristics of the stacks were evaluated by transmission electron microscopy and x-ray reflectivity measurements. The memory performance of the stacks was evaluated by write/erase and erase/write measurements, endurance and retention testing. It was found that in as-deposited state the Al2O3 layer is defective resulting in strong leakage currents, controlled by deep defects states. Thus, this behavior inhibits the memory functionality of the stacks. PDA crystallizes and condenses the Al2O3 transforming the layer from amorphous to polycrystalline. During this transformation the Al2O3 electrical quality improves greatly indicating that a significant number of these deep defects have been removed during annealing. Physical reasoning implies that the most plausible origin of these deep defects is hydrogen. However, the polycrystalline Al2O3 films showed inferior retention characteristics which are attributed to grain boundary related shallow defects. The findings of this work could pave the way for more efficient annealing schemes, in which an important factor is the time interval for hydrogen out-diffusion from the Al2O3 layer.

Influence of Graphene Interlayers on Electrode-Electrolyte Interfaces in Resistive Random Accesses Memory Cells

The behavior of the redox-based resistive switching memories is influenced by chemical interactions between the electrode and the solid electrolyte, as well as by local environment. The existence of different chemical potential gradients is resulting in nanobattery effect lowering the stability of the devices. In order to minimize these effects we introduce a graphene layer at the active electrode – solid electrolyte interface. We observe that graphene is acting as an effective diffusion barrier in the SiO2-based electrochemical metallization cells and acts catalytically on the electrochemical processes prior to resistive switching.

Extraction of the characteristics of Si nanocrystals by the charge pumping technique

In this paper, the characteristics of silicon nanocrystals used as charge trapping centers in memory devices are examined using the two-level charge pumping (CP) technique performed as a function of frequency and energy filtered transmission electron microscopy (EFTEM). The parameters extracted from the two methods such as the depth location, density and effective diameter of the nanocrystals are in good quantitative agreement. These results validate the charge pumping approach as a non-destructive powerful technique to access most of the properties of nanocrystals embedded in dielectrics and located at injection distances from the substrate surface not limited to the direct tunneling regime.

Temperature-dependent low electric field charging of Si nanocrystals embedded within oxide-nitride-oxide dielectric stacks

In this work we examine the current peaks and the negative differential resistance that appear in the low electric field regime of oxide-nitride-oxide structures with a two-dimensional band of silicon nanocrystals embedded in a nitride layer. The silicon nanocrystals were synthesized by low energy ion implantation (1 keV, 1.5 x 10(16) Si(+) cm(-2)) and subsequent thermal annealing (950 degrees C, 30 min). Electrical examination was performed at temperatures from 20 to 100 degrees C using constant voltage ramp-rate current measurements. This approach enables us to determine the origin of the observed current peaks as well as to extract the trapping location of the injected carriers within the dielectric stack. The results confirm that the carriers are trapped within the Si nanocrystal band, verifying that this region corresponds to energy minima of the dielectric stack.