Clinical effectiveness of a combination of oxiracetam and traditional Chinese medicine rehabilitation program in the treatment of early stroke patients with hemiplegia

Purpose: To evaluate the efficacy of a combination of oxiracetam and traditional Chinese medicine rehabilitation program on early stroke patients with hemiplegia. Methods: 120 patients with early stroke hemiplegia admitted to Wuhu Fifth People's Hospital from March 2019 to July 2020 were recruited. They were equally and randomly assigned to either a control group or a study group, using the random number table method. The control group received oxiracetam, while the study group received oxiracetam plus a traditional Chinese medicine (TCM) rehabilitation program. Outcome measures included treatment effectiveness, motor function, neurological function, TCM symptom scores, and patient satisfaction. Results: There was significantly higher treatment effectiveness in the study group versus the control group (p < 0.05). The Fugl-Meyer score of the control group was lower than that of the study group (52.49 ± 4.73 vs 74.73 ± 5.92; p < 0.001). After treatment, patients in the study group showed lower neurological function and TCM scores than those in the control group (p < 0.05). Furthermore, the study group showed higher satisfaction than the control group (p < 0.05). Conclusion: The combination of oxiracetam and TCM rehabilitation program produce good treatment effectiveness in early stroke hemiplegia patients, and also boosts motor and neurological functions when compared to the use of oxiracetam alone. However, the combination treatment should be subjected to further clinical trials prior to application in clinical practice. Keywords: Chinese medicine rehabilitation program; Early stroke hemiplegia; Oxiracetam; Motor function; Nerve functio

RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes

Nanopore sequencers generate electrical raw signals in real-time while sequencing long genomic strands. These raw signals can be analyzed as they are generated, providing an opportunity for real-time genome analysis. An important feature of nanopore sequencing, Read Until, can eject strands from sequencers without fully sequencing them, which provides opportunities to computationally reduce the sequencing time and cost. However, existing works utilizing Read Until either 1) require powerful computational resources that may not be available for portable sequencers or 2) lack scalability for large genomes, rendering them inaccurate or ineffective. We propose RawHash, the first mechanism that can accurately and efficiently perform real-time analysis of nanopore raw signals for large genomes using a hash-based similarity search. To enable this, RawHash ensures the signals corresponding to the same DNA content lead to the same hash value, regardless of the slight variations in these signals. RawHash achieves an accurate hash-based similarity search via an effective quantization of the raw signals such that signals corresponding to the same DNA content have the same quantized value and, subsequently, the same hash value. We evaluate RawHash on three applications: 1) read mapping, 2) relative abundance estimation, and 3) contamination analysis. Our evaluations show that RawHash is the only tool that can provide high accuracy and high throughput for analyzing large genomes in real-time. When compared to the state-of-the-art techniques, UNCALLED and Sigmap, RawHash provides 1) 25.8x and 3.4x better average throughput and 2) an average speedup of 32.1x and 2.1x in the mapping time, respectively. Source code is available at https://github.com/CMU-SAFARI/RawHash

Swordfish: A Framework for Evaluating Deep Neural Network-based Basecalling using Computation-In-Memory with Non-Ideal Memristors

Basecalling, an essential step in many genome analysis studies, relies on large Deep Neural Networks (DNNs) to achieve high accuracy. Unfortunately, these DNNs are computationally slow and inefficient, leading to considerable delays and resource constraints in the sequence analysis process. A Computation-In-Memory (CIM) architecture using memristors can significantly accelerate the performance of DNNs. However, inherent device non-idealities and architectural limitations of such designs can greatly degrade the basecalling accuracy, which is critical for accurate genome analysis. To facilitate the adoption of memristor-based CIM designs for basecalling, it is important to (1) conduct a comprehensive analysis of potential CIM architectures and (2) develop effective strategies for mitigating the possible adverse effects of inherent device non-idealities and architectural limitations. This paper proposes Swordfish, a novel hardware/software co-design framework that can effectively address the two aforementioned issues. Swordfish incorporates seven circuit and device restrictions or non-idealities from characterized real memristor-based chips. Swordfish leverages various hardware/software co-design solutions to mitigate the basecalling accuracy loss due to such non-idealities. To demonstrate the effectiveness of Swordfish, we take Bonito, the state-of-the-art (i.e., accurate and fast), open-source basecaller as a case study. Our experimental results using Sword-fish show that a CIM architecture can realistically accelerate Bonito for a wide range of real datasets by an average of 25.7x, with an accuracy loss of 6.01%.Comment: To appear in 56th IEEE/ACM International Symposium on Microarchitecture (MICRO), 202

GenPIP: In-Memory Acceleration of Genome Analysis via Tight Integration of Basecalling and Read Mapping

Nanopore sequencing is a widely-used high-throughput genome sequencing technology that can sequence long fragments of a genome into raw electrical signals at low cost. Nanopore sequencing requires two computationally-costly processing steps for accurate downstream genome analysis. The first step, basecalling, translates the raw electrical signals into nucleotide bases (i.e., A, C, G, T). The second step, read mapping, finds the correct location of a read in a reference genome. In existing genome analysis pipelines, basecalling and read mapping are executed separately. We observe in this work that such separate execution of the two most time-consuming steps inherently leads to (1) significant data movement and (2) redundant computations on the data, slowing down the genome analysis pipeline. This paper proposes GenPIP, an in-memory genome analysis accelerator that tightly integrates basecalling and read mapping. GenPIP improves the performance of the genome analysis pipeline with two key mechanisms: (1) in-memory fine-grained collaborative execution of the major genome analysis steps in parallel; (2) a new technique for early-rejection of low-quality and unmapped reads to timely stop the execution of genome analysis for such reads, reducing inefficient computation. Our experiments show that, for the execution of the genome analysis pipeline, GenPIP provides 41.6X (8.4X) speedup and 32.8X (20.8X) energy savings with negligible accuracy loss compared to the state-of-the-art software genome analysis tools executed on a state-of-the-art CPU (GPU). Compared to a design that combines state-of-the-art in-memory basecalling and read mapping accelerators, GenPIP provides 1.39X speedup and 1.37X energy savings.Comment: 17 pages, 13 figure

Natural Dibenzo-α-Pyrones and Their Bioactivities

Natural dibenzo-α-pyrones are an important group of metabolites derived from fungi, mycobionts, plants and animal feces. They exhibit a variety of biological activities such as toxicity on human and animals, phytotoxicity as well as cytotoxic, antioxidant, antiallergic, antimicrobial, antinematodal, and acetylcholinesterase inhibitory properties. Dibenzo-α-pyrones are biosynthesized via the polyketide pathway in microorganisms or metabolized from plant-derived ellagitannins and ellagic acid by intestinal bacteria. At least 53 dibenzo-α-pyrones have been reported in the past few decades. This mini-review aims to briefly summarize the occurrence, biosynthesis, biotransformation, as well as their biological activities and functions. Some considerations related to synthesis, production and applications of dibenzo-α-pyrones are also discussed

Enhancement of Palmarumycins C12 and C13 Production in Liquid Culture of Endophytic Fungus Berkleasmium sp. Dzf12 after Treatments with Metal Ions

The influences of eight metal ions (i.e., Na+, Ca2+, Ag+, Co2+, Cu2+, Al3+, Zn2+, and Mn4+) on mycelia growth and palmarumycins C12 and C13 production in liquid culture of the endophytic fungus Berkleasmium sp. Dzf12 were investigated. Three metal ions, Ca2+, Cu2+ and Al3+ were exhibited as the most effective to enhance mycelia growth and palmarumycin production. When calcium ion (Ca2+) was applied to the medium at 10.0 mmol/L on day 3, copper ion (Cu2+) to the medium at 1.0 mmol/L on day 3, aluminum ion (Al3+) to the medium at 2.0 mmol/L on day 6, the maximal yields of palmarumycins C12 plus C13 were obtained as 137.57 mg/L, 146.28 mg/L and 156.77 mg/L, which were 3.94-fold, 4.19-fold and 4.49-fold in comparison with that (34.91 mg/L) of the control, respectively. Al3+ favored palmarumycin C12 production when its concentration was higher than 4 mmol/L. Ca2+ had an improving effect on mycelia growth of Berkleasmium sp. Dzf12. The combination effects of Ca2+, Cu2+ and Al3+ on palmarumycin C13 production were further studied by employing a statistical method based on the central composite design (CCD) and response surface methodology (RSM). By solving the quadratic regression equation between palmarumycin C13 and three metal ions, the optimal concentrations of Ca2+, Cu2+ and Al3+ in medium for palmarumycin C13 production were determined as 7.58, 1.36 and 2.05 mmol/L, respectively. Under the optimum conditions, the predicted maximum palmarumycin C13 yield reached 208.49 mg/L. By optimizing the combination of Ca2+, Cu2+ and Al3+ in medium, palmarumycin C13 yield was increased to 203.85 mg/L, which was 6.00-fold in comparison with that (33.98 mg/L) in the original basal medium. The results indicate that appropriate metal ions (i.e., Ca2+, Cu2+ and Al3+) could enhance palmarumycin production. Application of the metal ions should be an effective strategy for palmarumycin production in liquid culture of the endophytic fungus Berkleasmium sp. Dzf12

Enhancement of Palmarumycins C12 and C13 Production in Liquid Culture of Endophytic Fungus Berkleasmium sp. Dzf12 after Treatments with Metal Ions

and Mn 4+) on mycelia growth and palmarumycins C12 and C13 production in liquid culture of the endophytic fungus Berkleasmium sp. Dzf12 were investigated. Three metal ions, Ca 2+, Cu 2+ and Al 3+ were exhibited as the most effective to enhance mycelia growth and palmarumycin production. When calcium ion (Ca 2+) was applied to the medium at 10.0 mmol/L on day 3, copper ion (Cu 2+) to the medium at 1.0 mmol/L on day 3, aluminum ion (Al 3+) to the medium at 2.0 mmol/L on day 6, the maximal yields of palmarumycins C12 plus C13 were obtained as 137.57 mg/L, 146.28 mg/L and 156.77 mg/L, which were 3.94-fold, 4.19-fold and 4.49-fold in comparison with that (34.91 mg/L) of the control, respectively. Al 3+ favored palmarumycin C12 production when its concentration was higher than 4 mmol/L. Ca 2+ had an improving effect on mycelia growth of Berkleasmium sp. Dzf12. The combination effects of Ca 2+, Cu 2+ and Al 3+ on palmarumycin C13 production were further studied by employing a statistical method based on the central composite design (CCD) and response surface methodology (RSM). By solving the quadratic regression equation between palmarumycin C13 and three metal ions, the optima

NEON: Enabling Efficient Support for Nonlinear Operations in Resistive RAM-based Neural Network Accelerators

Resistive Random-Access Memory (RRAM) is well-suited to accelerate neural network (NN) workloads as RRAM-based Processing-in-Memory (PIM) architectures natively support highly-parallel multiply-accumulate (MAC) operations that form the backbone of most NN workloads. Unfortunately, NN workloads such as transformers require support for non-MAC operations (e.g., softmax) that RRAM cannot provide natively. Consequently, state-of-the-art works either integrate additional digital logic circuits to support the non-MAC operations or offload the non-MAC operations to CPU/GPU, resulting in significant performance and energy efficiency overheads due to data movement. In this work, we propose NEON, a novel compiler optimization to enable the end-to-end execution of the NN workload in RRAM. The key idea of NEON is to transform each non-MAC operation into a lightweight yet highly-accurate neural network. Utilizing neural networks to approximate the non-MAC operations provides two advantages: 1) We can exploit the key strength of RRAM, i.e., highly-parallel MAC operation, to flexibly and efficiently execute non-MAC operations in memory. 2) We can simplify RRAM's microarchitecture by eliminating the additional digital logic circuits while reducing the data movement overheads. Acceleration of the non-MAC operations in memory enables NEON to achieve a 2.28x speedup compared to an idealized digital logic-based RRAM. We analyze the trade-offs associated with the transformation and demonstrate feasible use cases for NEON across different substrates

Enhanced Electrochemical Performance of PEO-Based Composite Polymer Electrolyte with Single-Ion Conducting Polymer Grafted SiO₂ Nanoparticles

In order to enhance the electrochemical performance and mechanical properties of poly(ethylene oxide) (PEO)-based solid polymer electrolytes, composite solid electrolytes (CSE) composed of single-ion conducting polymer-modified SiO2, PEO and lithium salt were prepared and used in lithium-ion batteries in this work. The pyridyl disulfide terminated polymer (py-ss-PLiSSPSI) is synthesized through RAFT polymerization, then grafted onto SiO2 via thiol-disulfide exchange reaction between SiO2-SH and py-ss-PLiSSPSI. The chemical structure, surface morphology and elemental distribution of the as-prepared polymer and the PLiSSPSI-g-SiO2 nanoparticles have been investigated. Moreover, CSEs containing 2, 6, and 10 wt% PLiSSPSI-g-SiO2 nanoparticles (PLi-g-SiCSEs) are fabricated and characterized. The compatibility of the PLiSSPSI-g-SiO2 nanoparticles and the PEO can be effectively improved owing to the excellent dispersibility of the functionalized nanoparticles in the polymer matrix, which promotes the comprehensive performances of PLi-g-SiCSEs. The PLi-g-SiCSE-6 exhibits the highest ionic conductivity (0.22 mS·cm−1) at 60 °C, a large tLi+ of 0.77, a wider electrochemical window of 5.6 V and a rather good lithium plating/stripping performance at 60 °C, as well as superior mechanical properties. Hence, the CSEs containing single-ion conducting polymer modified nanoparticles are promising candidates for all-solid-state lithium-ion batteries

From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures

We now need more than ever to make genome analysis more intelligent. We need to read, analyze, and interpret our genomes not only quickly, but also accurately and efficiently enough to scale the analysis to population level. There currently exist major computational bottlenecks and inefficiencies throughout the entire genome analysis pipeline, because state-of-the-art genome sequencing technologies are still not able to read a genome in its entirety. We describe the ongoing journey in significantly improving the performance, accuracy, and efficiency of genome analysis using intelligent algorithms and hardware architectures. We explain state-of-the-art algorithmic methods and hardware-based acceleration approaches for each step of the genome analysis pipeline and provide experimental evaluations. Algorithmic approaches exploit the structure of the genome as well as the structure of the underlying hardware. Hardware-based acceleration approaches exploit specialized microarchitectures or various execution paradigms (e.g., processing inside or near memory) along with algorithmic changes, leading to new hardware/software co-designed systems. We conclude with a foreshadowing of future challenges, benefits, and research directions triggered by the development of both very low cost yet highly error prone new sequencing technologies and specialized hardware chips for genomics. We hope that these efforts and the challenges we discuss provide a foundation for future work in making genome analysis more intelligent.ISSN:2001-037