Screening and Application of Microorganisms That Inhibit Cyanobacteria Blooms

Cyanobacterial blooms are a global ecological problem. The purpose of this study is to find microbial strains that can be used for the biological control of cyanobacterial blooms. In this study, a strain of Lysinibacillus fusiformis (QY-12) with the best algae inhibition effect was isolated and screened. The inhibition rate of Microcystis aeruginosa in the growth index period was 82.01% after 8 days of interference by QY-12. The algicidal substance l-2-aminoadipic acid (L-2-AA) produced by QY-12 was purified via preparative HPLC and identified by nuclear magnetic resonance (NMR), liquid chromatography-mass spectrometry (LC-MS) and infrared spectroscopy (IR). It was found by scanning electron microscopy that L-2-AA had a significant inhibitory effect on Microcystis aeruginosa in the early and middle stages of growth. This study provides a new idea for the prevention and control of cyanobacterial blooms

Inverse-designed Photonic Computing Core for Parallel Matrix-vector Multiplication

On-chip optical neural networks (ONNs) have recently emerged as an attractive hardware accelerator for deep learning applications, characterized by high computing density, low latency, and compact size. As these networks rely heavily on massive matrix multiplication, photonic computing cores for matrix computation become crucial components for on-chip ONNs, which harness the degree of freedoms (DOFs) in photonics including space, wavelength and mode dimensions. However, previous photonic computing devices have not fully utilized the orthogonality and the conversion characteristic of the waveguide modes, which as we show here, allows for the simultaneous parallel computing of several independent matrix-vector multiplications within the same device. In this work, we propose an inverse-designed photonic computing core for parallel matrix-vector multiplication. The matrices are implemented through a mode conversion process, where the input fundamental modes are simultaneously converted into several orthogonal output modes. Specifically, we target the complex-valued conversion matrices between input and output modes and inversely design the dielectric distribution within the device to achieve parallel matrix-vector multiplication. As a demonstration, the proposed photonic computing core supports simultaneous parallel computing of two independent matrix-vector multiplications, with an ultra-compact footprint and high computing precision (relative error < 8%) at 1550 nm wavelength. The inverse-designed photonic computing devices hold great potential for high-performance on-chip ONNs with low energy consumption and high computing density

The relationship with insulin resistance of each combination of TG/HDL-C tertiles and WC quartiles.

<p>Each block of OR represents the OR as against the OR which the patients belonging to the first tertile of TG/HDL-C and the first quartile of WC. Abbreviations: WC:waist circumference; T:tertile; Q:quartile.</p

Association between Triglyceride to HDL-C Ratio (TG/HDL-C) and Insulin Resistance in Chinese Patients with Newly Diagnosed Type 2 Diabetes Mellitus

<div><p>Objectives</p><p>To explore the association between the triglyceride to HDL-C ratio (TG/HDL-C) and insulin resistance in Chinese patients with newly diagnosed type 2 diabetes mellitus.</p><p>Methods</p><p>Patients with newly diagnosed type 2 diabetes mellitus (272 men and 288 women) were enrolled and divided into three groups according to TG/HDL-C tertiles. Insulin resistance was defined by homeostatic model assessment of insulin resistance (HOMA-IR). Demographic information and clinical characteristics were obtained. Spearman’s correlation was used to estimate the association between TG/HDL-C and other variables. Multiple logistic regression analyses were adopted to obtain probabilities of insulin resistance. A receiver operating characteristic analysis was conducted to evaluate the ability of TG/HDL-C to discriminate insulin resistance.</p><p>Results</p><p>TG/HDL-C was associated with insulin resistance in Chinese patients with newly diagnosed T2DM (Spearman’s correlation coefficient = 0.21, P < 0.01). Patients in the higher tertiles of TG/HDL-C had significantly higher HOMA-IR values than patients in the lower tertiles [T1: 2.68(1.74–3.70); T2: 2.96(2.29–4.56); T3: 3.09(2.30–4.99)]. Multiple logistic regression analysis showed that TG/HDL-C was significantly associated with HOMA-IR, and patients in the higher TG/HDL-C tertile had a higher OR than those in the lower TG/HDL-C tertile, after adjusting for multiple covariates including indices for central obesity [T1: 1; T2: 4.02(1.86–8.71); T3: 4.30(1.99–9.29)]. Following stratification of waist circumference into quartiles, the effect of TG/HDL-C on insulin resistance remained significant irrespective of waist circumference.</p><p>Conclusions</p><p>TG/HDL-C was associated with insulin resistance independent of waist circumference. Whether it could be a surrogate marker for insulin resistance in Chinese patients with newly diagnosed type 2 diabetes mellitus still needs to be confirmed by more researches.</p></div

The representive case who suffered heterotopic ossification of Discover prosthesis postoperatively.

<p>Dynamic flexion-extension lateral radiographs at the final follow-up showing heterotopic ossification (Grade III) at the index level with significant restriction in the range of motion of Discover prothesis.</p

Dysphagia, prothesis migration, subsidence and adjacent segment degeneration of patients between the 2 groups.

<p>Dysphagia, prothesis migration, subsidence and adjacent segment degeneration of patients between the 2 groups.</p

Comparison of clinical outcomes between the 2 groups.

<p>Comparison of clinical outcomes between the 2 groups.</p

Demographic data of patients.

<p>Demographic data of patients.</p

Clinical and metabolic characteristics of patients according to TG/HDL-C tertiles.

<p>Clinical and metabolic characteristics of patients according to TG/HDL-C tertiles.</p

The methods about measurements of radiographic parameters.

<p>The range of motion (ROM) at the adjacent levels by disc space angle in the Discover group (a). The overall alignment of C2-C7 (α,OSA) and functional spinal unit (β,FSU) angle in the Discover group (b). The range of motion (ROM) at the adjacent levels by disc space angle in the Zero-P group (c). The overall alignment of C2-C7 (α,OSA) and functional spinal unit (β,FSU) angle in the Zero-P group (d).</p