Flexible Gel-Free Multi-Modal Wireless Sensors With Edge Deep Learning for Detecting and Alerting Freezing of Gait Symptom.

Freezing of gait (FoG) is a debilitating symptom of Parkinson's disease (PD). This work develops flexible wearable sensors that can detect FoG and alert patients and companions to help prevent falls. FoG is detected on the sensors using a deep learning (DL) model with multi-modal sensory inputs collected from distributed wireless sensors. Two types of wireless sensors are developed, including: 1) a C-shape central node placed around the patient's ears, which collects electroencephalogram (EEG), detects FoG using an on-device DL model, and generates auditory alerts when FoG is detected; 2) a stretchable patch-type sensor attached to the patient's legs, which collects electromyography (EMG) and movement information from accelerometers. The patch-type sensors wirelessly send collected data to the central node through low-power ultra-wideband (UWB) transceivers. All sensors are fabricated on flexible printed circuit boards. Adhesive gel-free acetylene carbon black and polydimethylsiloxane electrodes are fabricated on the flexible substrate to allow conformal wear over the long term. Custom integrated circuits (IC) are developed in 180 nm CMOS technology and used in both types of sensors for signal acquisition, digitization, and wireless communication. A novel lightweight DL model is trained using multi-modal sensory data. The inference of the DL model is performed on a low-power microcontroller in the central node. The DL model achieves a high detection sensitivity of 0.81 and a specificity of 0.88. The developed wearable sensors are ready for clinical experiments and hold great promise in improving the quality of life of patients with PD. The proposed design methodologies can be used in wearable medical devices for the monitoring and treatment of a wide range of neurodegenerative diseases.

Improved Cycling of Li||NMC811 Batteries under Practical Conditions by a Localized High-Concentration Electrolyte.

Li||NMC811 battery, with lithium-metal (high specific capacity and low redox potential) as anode and LiNi0.8 Co0.1 Mn0.1 O2 (NMC811) as cathode, has been widely accepted to be a good candidate as one of the high-energy-density batteries. However, its cyclability needs improvement to fulfill the requirement for its future commercial use, especially under practical conditions. Electrolyte plays a key role in improving the cycling performance of Li||NMC811 batteries, where a high voltage/electrochemical window and good stability with the electrodes of the electrolyte are required. Herein, a localized high-concentration electrolyte with an additive of lithium difluoro(oxalate)borate (LiDFOB) is reported that improves the cycling performance of Li||NMC811 cells under crucial conditions with Li foil thickness of 50 µm, cathode areal loading of 4 mAh cm-2 , the areal capacity ratio between the negative and positive electrodes (N/P ratio) of 2.6 and the electrolyte/cell capacity ratio (E/C ratio) of 3.0 g (Ah)-1 . These cells can maintain 80% of the capacity after 195 cycles.

Associations of inflammatory cytokines with inflammatory bowel disease: a Mendelian randomization study.

Objectives: Previous studies have confirmed a link between specific inflammatory cytokines and inflammatory bowel disease (IBD), but the causal relationship between them is not completely clear. This Mendelian Randomization (MR) study aims to evaluate the causal relationship between 18 inflammatory cytokines and inflammatory bowel disease. Method: Two-sample Mendelian randomization utilized genetic variances associated with IBD from two extensive publicly available genome-wide association studies (GWAS) (Crohn's Disease (CD): 12,194 cases and 28,072 controls; Ulcerative Colitis (UC): 12,336 cases and 33,609 controls). The data of inflammatory cytokines was acquired from a GWAS including 8,293 healthy participants. We used inverse variance weighted method, MR-Egger, weighted median, simple model and weighted model to evaluate the causal relationship between inflammatory cytokines and IBD. Sensitivity analysis includes heterogeneity and pleiotropy analysis to evaluate the robustness of the results. Results: The findings indicated suggestive positive associations between Interleukin-13 (IL-13) and macrophage migration inhibitory factor (MIF) with CD (odds ratio, OR: 1.101, 95%CI: 1.021-1.188, p = 0.013; OR: 1.134, 95%CI: 1.024-1.255, p = 0.015). IL-13 also displayed a significant positive correlation with UC (OR: 1.099, 95%CI: 1.018-1.186, p = 0.016). Stem cell factor (SCF) was suggested to be associated with the development of both CD and UC (OR: 1.032, 95%CI: 0.973-1.058, p = 0.012; OR: 1.038, 95%CI: 1.005-1.072, p = 0.024). Conclusion: This study proposes that IL-13 may be a factor correlated with the etiology of IBD (CD and UC), while MIF just be specifically associated with CD. Additionally, SCF appears more likely to be involved in the downstream development of IBD (CD and UC).

Design of a Sleep Modulation System with FPGA-Accelerated Deep Learning for Closed-loop Stage-Specific In-Phase Auditory Stimulation.

Closed-loop sleep modulation is an emerging research paradigm to treat sleep disorders and enhance sleep benefits. However, two major barriers hinder the widespread application of this research paradigm. First, subjects often need to be wire-connected to rack-mount instrumentation for data acquisition, which negatively affects sleep quality. Second, conventional real-time sleep stage classification algorithms give limited performance. In this work, we conquer these two limitations by developing a sleep modulation system that supports closed-loop operations on the device. Sleep stage classification is performed using a lightweight deep learning (DL) model accelerated by a low-power field-programmable gate array (FPGA) device. The DL model uses a single channel electroencephalogram (EEG) as input. Two convolutional neural networks (CNNs) are used to capture general and detailed features, and a bidirectional long-short-term memory (LSTM) network is used to capture time-variant sequence features. An 8-bit quantization is used to reduce the computational cost without compromising performance. The DL model has been validated using a public sleep database containing 81 subjects, achieving a state-of-the-art classification accuracy of 85.8% and a F1-score of 79%. The developed model has also shown the potential to be generalized to different channels and input data lengths. Closed-loop in-phase auditory stimulation has been demonstrated on the test bench.

High-nuclear polyoxovanadates assembled from pentagonal building blocks.

The design and synthesis of high-nuclear polyoxometalates (POMs) constructed from pentagonal molecular building blocks (MBBs) are attractive and challenging. Herein, three new high-nuclear polyoxovanadates, including {V20W2P20} (1), {V18W4P14} (2), and {V26W6P16} (3), have been successfully synthesized under solvothermal conditions. All these structures are assembled from pentagonal MBB {WV5(PhPO3)5} with different configurations. Compound 1 exhibits efficient, stable, and versatile catalytic activity for sulfide oxidation.

Expressivity of Interleukin-8 and Gastric Cancer Prognosis Susceptibility: A Systematic Review and Meta-Analysis.

BACKGROUND: The relationship between interleukin-8 (IL-8) expression and the prognosis of gastric cancer (GC) patients has been reported, but the results are contradictory. AIM: To investigate the effect of IL-8 expression on the prognosis of patients with GC. METHOD: A comprehensive search strategy was used to search the PubMed, Web of Science and Cochrane Library databases. The total survival time was analysed using the RevMan 5.4 software. Through extensive search and meta-analysis of relevant studies, studies examining the relationship between IL-8 expression and prognosis in patients with GC were conducted to obtain more accurate estimates. FINDINGS: Eight studies (1843 patients) were included. The combined results of all the studies showed that high expression of IL-8 was a risk factor for poor prognosis in patients with GC (hazard ratio (HR): 2.08; 95% CI: 1.81-2.39). Sensitivity analysis suggested that the pooled HR was stable, and omitting a single study did not change the significance of the pooled HR. Funnel plots revealed no significant publication bias in the meta-analysis. CONCLUSION: High IL-8 expression could be a negative prognostic biomarker for patients with GC.

Phylogenetic and morphological analyses of the mycoparasitic genus Piptocephalis.

The Piptocephalidaceae (Zoopagales, Zoopagomycota) contains three genera of mycoparasitic, haustoria-forming fungi: Kuzuhaea, Piptocephalis, and Syncephalis. Although the species in this family are diverse and ubiquitous in soil and dung, they are among the least studied fungi. Co-cultures of Piptocephalis and their hosts are relatively easy to isolate from soil and dung samples across the globe, making them a good model taxon for the order Zoopagales. This study focuses on the systematics of the genus Piptocephalis. Despite the fact that there are approximately 40 described Piptocephalis species, there are no modern taxonomic or molecular phylogenetic treatments of this group. Minimal sequence data are available, and relatively little is known about the true diversity or biogeography of the genus. Our study addresses two aspects: Piptocephalis systematics and analyses of the length and inter- and infraspecific variation of the nuc rDNA internal transcribed spacer (ITS1-5.8S-ITS2 = ITS) region. First, we generated a large subunit (28S) nuc rDNA phylogeny and evaluated several morphological characters by testing their correlation with the phylogeny using Bayesian Tip-association Significance testing (BaTS). We found monophyly of Piptocephalis species identified based on morphological traits, but morphological character states were not conserved across clades, suggesting that there have been multiple gains and losses of morphological characters. We also found that Kuzhuaea is nested within Piptocephalis. Second, we amplified the ITS from many Piptocephalis isolates, created a sequence alignment, and measured the lengths using the software ITSx. Piptocephalis species had ITS regions that were longer than the average for most Dikarya but were similar in length to those of the related genus Syncephalis.

Constructing a cellulosic yeast host with an efficient cellulase cocktail.

Cellulose is a renewable feedstock for green industry. It is therefore important to develop a technique to construct a host with a high cellulolytic efficiency to digest cellulose. In this study, we developed a convenient host-engineering technique to adjust the expression levels of heterologous genes in the host by promoter rearrangement and gene copy number adjustment. Using genes from different glycoside hydrolase (GH) families including GH2, GH3, GH5, GH6, GH7, and GH12 from Aspergillus niger, Trichoderma reesei, and Neocallimastix patriciarum, we constructed a cellulolytic Kluyveromyces marxianus with eight cellulase gene-cassettes that produced a cellulase cocktail with a high cellulolytic efficiency, leading to a significant reduction in enzyme cost in a rice straw saccharification process. Our technique can be used to design a host that can efficiently convert biomass feedstock to biofuel.

Assembling a cellulase cocktail and a cellodextrin transporter into a yeast host for CBP ethanol production.

BACKGROUND: Many microorganisms possess enzymes that can efficiently degrade lignocellulosic materials, but do not have the capability to produce a large amount of ethanol. Thus, attempts have been made to transform such enzymes into fermentative microbes to serve as hosts for ethanol production. However, an efficient host for a consolidated bioprocess (CBP) remains to be found. For this purpose, a synthetic biology technique that can transform multiple genes into a genome is instrumental. Moreover, a strategy to select cellulases that interact synergistically is needed. RESULTS: To engineer a yeast for CBP bio-ethanol production, a synthetic biology technique, called "promoter-based gene assembly and simultaneous overexpression" (PGASO), that can simultaneously transform and express multiple genes in a kefir yeast, Kluyveromyces marxianus KY3, was recently developed. To formulate an efficient cellulase cocktail, a filter-paper-activity assay for selecting heterologous cellulolytic enzymes was established in this study and used to select five cellulase genes, including two cellobiohydrolases, two endo-ß-1,4-glucanases and one beta-glucosidase genes from different fungi. In addition, a fungal cellodextrin transporter gene was chosen to transport cellodextrin into the cytoplasm. These six genes plus a selection marker gene were one-step assembled into the KY3 genome using PGASO. Our experimental data showed that the recombinant strain KR7 could express the five heterologous cellulase genes and that KR7 could convert crystalline cellulose into ethanol. CONCLUSION: Seven heterologous genes, including five cellulases, a cellodextrin transporter and a selection marker, were simultaneously transformed into the KY3 genome to derive a new strain, KR7, which could directly convert cellulose to ethanol. The present study demonstrates the potential of our strategy of combining a cocktail formulation protocol and a synthetic biology technique to develop a designer yeast host.