5-Aminolevulinic acid (ALA), a nonprotein amino acid involved in tetrapyrrole synthesis, has been widely applied in agriculture, medicine, and food production. Many engineered metabolic pathways have been constructed; however, the production yields are still low. In this study, several 5-aminolevulinic acid synthases (ALASs) from different sources were evaluated and compared with respect to their ALA production capacities in an engineered Corynebacterium glutamicum CgS1 strain that can accumulate succinyl-coenzyme A (CoA). A codon-optimized ALAS from Rhodobacter capsulatus SB1003 displayed the best potential. Recombinant strain CgS1/pEC-SB produced 7.6 g/liter ALA using a mineral salt medium in a fed-batch fermentation mode. Employing two-stage fermentation, 12.46 g/liter ALA was produced within 17 h, with a productivity of 0.73 g/liter/h, in recombinant C. glutamicum. Through overexpression of the heterologous nonspecific ALA exporter RhtA from Escherichia coli, the titer was further increased to 14.7 g/liter. This indicated that strain CgS1/pEC-SB-rhtA holds attractive industrial application potential for the future. IMPORTANCEIn this study, a two-stage fermentation strategy was used for production of the value-added nonprotein amino acid 5-aminolevulinic acid from glucose and glycine in a generally recognized as safe (GRAS) host, Corynebacterium glutamicum. The ALA titer represented the highest in the literature, to our knowledge. This high production capacity, combined with the potential easy downstream processes, made the recombinant strain an attractive candidate for industrial use in the future.A s the common precursor of tetrapyrroles such as porphyrin, heme, vitamin B 12 , and chlorophyll, 5-aminolevulinic acid (ALA) has been reported to be effective in tumor-localizing and photodynamic therapy for various diseases (1-3). ALA can also be used as a selective biodegradable herbicide and insecticide or an adversity resistance and growth-accelerating agent in agriculture (4, 5).In living organisms, two kinds of metabolic pathways have been described for ALA biosynthesis (Fig. 1). One is the C 5 pathway, which occurs in algae, higher plants, and many bacteria, including Escherichia coli and archaea. The C 5 pathway involves the following three enzymatic activities: glutamyl-tRNA synthetase (GluRS) (encoded by gltX), a NADPH-dependent glutamyl-tRNA reductase (HemA, encoded by hemA), and a glutamate-1-semialdehyde aminotransferase (HemL, encoded by hemL). The other is the C 4 pathway, which is present in birds, mammals, yeast, and purple non-sulfur-photosynthetic bacteria. In this pathway, ALA is formed through one-step catalysis by 5-aminolevulinic acid synthase (ALAS), which condenses glycine and succinyl-coenzyme A (CoA), an intermediate of the tricarboxylic acid (TCA) cycle.In E. coli, the native pathway for ALA biosynthesis is the C 5 pathway, which is tightly regulated by feedback inhibition of the end product heme (6). Previously, we developed a strategy to produce ALA in recombinant E. coli vi...

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In order to predict related risk factors for lymph node metastasis (LNM) in patients with superficial esophageal carcinoma (SEC) and provide reference for endoscopic minimally invasive treatment, we included a total of 93 patients with superficial esophageal carcinoma who have underwent esophagectomy and lymph node dissection from 2010 to 2015. The depth of invasion was remeasured and classified into 6 groups according to their wall penetration. The prediction model was founded based on the independent risk factors. The results shows that lymph node metastasis of m1, m2, m3, sm1, sm2, and sm3 of superficial esophageal carcinoma was 0%, 0%, 5.3%, 8.7%, 17.6%, and 37.5%, respectively. The tumor size, differentiation, and lymphvascular invasion were also significantly related to lymph node metastasis by univariate analysis. Multivariate analysis showed that the depth of invasion and lymphovascular invasion were independent risk factors of lymph node metastasis. A prediction model for lymph node metastasis was established as follows: p = e x/(1 + e x), and x = −5.469 + 0.839 × depth of invasion + 1.992 × lymphavascular metastasis. The area under ROC curve was 0.858 (95% CI: 0.757–0.959). It was also shown that the depth of invasion was related to tumor differentiation, macroscopic type, and tumor size.

Spectrum sensing is one of the key technologies in the field of cognitive radio, which has been widely studied. Among all the sensing methods, energy detection is the most popular because of its simplicity and no requirement of any prior knowledge of the signal. In the case of low signal-to-noise ratio (SNR), the traditional double-threshold energy detection method employs fixed thresholds and there is no detection result when the energy is between high and low thresholds, which leads to poor detection performance such as lower detection probability and longer spectrum sensing time. To address these problems, we proposed an adaptive double-threshold cooperative spectrum sensing algorithm based on history energy detection. In each sensing period, we calculate the weighting coefficient of thresholds according to the SNR of all cognitive nodes; thus, the upper and lower thresholds can be adjusted adaptively. Furthermore, in a single cognitive node, once the current energy is within the high and low thresholds, we utilize the average energy of history sensing times to rejudge. To ensure the real-time performance, if the average history energy is still between two thresholds, the single-threshold method will be used for the end decision. Finally, the fusion center aggregates the detection results of each node and obtains the final cooperative conclusion through “or” criteria. Theoretical analysis and simulation results show that the algorithm proposed in this paper improved detection performance significantly compared with the other four different double-threshold algorithms.