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In this study, we presented the efficiently adsorptive removal of organic dye, methyl orange (MO), from aqueous solution with the hydrothermal synthesized Mg–Al layered double hydroxide (LDH). The as-obtained product was characterized by X-ray diffraction (XRD), Fourier transformation infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). The adsorption characteristics of MO onto the Mg–Al LDH were evaluated in a batch adsorption process and systematically investigated by different experimental parameters, such as initial dye concentration, contact time, and solution pH. The adsorption kinetics were analyzed by pseudofirst-order, pseudosecond-order, Elovich, intraparticle diffusion, and Boyd models, which were well-described by the pseudosecond-order model. The equilibrium adsorption data were interpreted using the Langmuir, Freundlich, and Temkin isotherm models, which fitted well to both the Langmuir and Freundlich models. The monolayer adsorption capacity of the Mg–Al LDH toward MO was found to be 0.453 mol·kg–1. These findings suggested that the LDH could be regarded as a promising adsorbent for the removal of anionic dye in wastewaters.

Antibody drug conjugates (ADCs) are monoclonal antibodies designed to deliver a cytotoxic drug selectively to antigen expressing cells. Several components of an ADC including the selection of the antibody, the linker, the cytotoxic drug payload and the site of attachment used to attach the drug to the antibody are critical to the activity and development of the ADC.The cytotoxic drugs or payloads used to make ADCs are typically conjugated to the antibody through cysteine or lysine residues. This results in ADCs that have a heterogeneous number of drugs per antibody. The number of drugs per antibody commonly referred to as the drug to antibody ratio (DAR), can vary between 0 and 8 drugs for a IgG1 antibody. Antibodies with 0 drugs are ineffective and compete with the ADC for binding to the antigen expressing cells. Antibodies with 8 drugs per antibody have reduced in vivo stability, which may contribute to non target related toxicities.In these studies we incorporated a non-natural amino acid, para acetyl phenylalanine, at two unique sites within an antibody against Her2/neu. We covalently attached a cytotoxic drug to these sites to form an ADC which contains two drugs per antibody.We report the results from the first direct preclinical comparison of a site specific non-natural amino acid anti-Her2 ADC and a cysteine conjugated anti-Her2 ADC. We report that the site specific non-natural amino acid anti-Her2 ADCs have superior in vitro serum stability and preclinical toxicology profile in rats as compared to the cysteine conjugated anti-Her2 ADCs. We also demonstrate that the site specific non-natural amino acid anti-Her2 ADCs maintain their in vitro potency and in vivo efficacy against Her2 expressing human tumor cell lines. Our data suggests that site specific non-natural amino acid ADCs may have a superior therapeutic window than cysteine conjugated ADCs.

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Background and Purpose-In vivo and in vitro rat models of hormone therapy were used to test the following hypotheses:(1) estrogen acts directly on cerebrovascular estrogen receptors to increase endothelial nitric oxide synthase (eNOS); (2) increased protein correlates with higher NOS activity; and (3) effects of estrogen on eNOS are altered by concurrent treatment with either medroxyprogesterone acetate (MPA) or progesterone. Methods-Blood vessels were isolated from brains of ovariectomized female rats; some were treated for 1 month with estrogen, estrogen and progesterone, or estrogen and MPA. The latter effect persists in the presence of either progesterone or MPA. Thus, increased NO production by eNOS may contribute to the neuroprotective effects of estrogen.

With the advent of global 5G networks, the Internet of Things will no longer be limited by network speed and traffic. With the large-scale application of the Internet of Things, people pay more and more attention to the security of the Internet of Things. Once the Internet of Things system suffers from malicious attacks, not only the serious loss of information will lead to the paralysis of the Internet of Things equipment. Aiming at the security problem of the Internet of Things, this paper puts forward the LM-BP neural network model. The LM-BP neural network model is applied to an intrusion detection system, and the intrusion detection flow under LM-BP algorithm is given. LM algorithm has the characteristics of fast optimization speed and strong robustness and uses this characteristic to optimize the weight threshold of traditional BP neural network. Through establishing LM-BP neural network classifier, KDD CUP 99 intrusion detection data set is imported into an LM-BP neural network classifier, and the best results are obtained through continuous training. Finally, the experimental simulation results show that this model has higher detection rate and lower false alarm rate than the traditional BP neural network model and PSO-BP neural network model for DOS, R2L, U2L, and Probing, thus this modified model has certain promotion value.INDEX TERMS Intrusion detection system, KDD CUP 99 dataset, LM-BP neural network model.