Milk Oligopeptide Inhibition of (α)-Tocopherol Fortified Linoleic Acid Oxidation

This study investigated the effect of milk oligopeptides and (α)-tocopherol on inhibition of linoleic acid oxidation using Fe2+-vitamin C induced linoleic acid oxidation model through analysis of malondialdehyde, peroxide value, and conjugated diene and triene in the model. The alteration of milk oligopeptides maximal absorption wavelength, fluorescent feature, and secondary structure were further investigated to elucidate the interactions between milk oligopeptide and (α)-tocopherol that altered the inhibitory effect of linoleic acid oxidation. Results showed that Pro-Tyr-Tyr-Ala-Lys (PYYAK) and Ile-Pro-Ile-Gln-Tyr (IPIQY) with (α)-tocopherol significantly inhibited the oxidation of linoleic acid and reduced the formation of malondialdehyde by 38% and 41%, respectively. Additionally, Ile-Pro-Ile-Gln-Tyr-Val (IPIQYV) and (α)-tocopherol synergistically reduced the peroxide value in the model by 36.8%. Milk oligopeptides exhibited a blue shift on its maximal absorption wavelength, and their absorbance value decreased with the increase of the (α)-tocopherol concentration. The fluorescent intensity of milk oligopeptides was reduced with the addition of (α)-tocopherol and such fluorescent intensity reductions resulted from the static quenching process through the formation of milk oligopeptide-(α)-tocopherol complex. Fourier transform infrared spectroscopy analysis revealed that (α)-tocopherol significantly altered the secondary structure of milk oligopeptides and the percentage of β-turn obviously increased in milk oligopeptide-(α)-tocopherol complex. These indicated that the inhibition of linoleic acid oxidation might result from complex formed between milk oligopeptide and (α)-tocopherol through inter-molecular interactions

1 Control Transmission Pace at IP Layer to Avoid Packet Drop

Abstract — Avoiding packet loss is critical for time sensitive network applications, such as multimedia streams for video/voice. Delaying and dropping low priority packets to ensure high priority and time sensitive data stream deliver during network congestion is a basic QoS (quality of service) mechanism over current network infrastructure. This mechanism works if time sensitive data stream is the minority of the network traffic and if the network is not very congested. The methodology of dropping low priority data will not scale when time sensitive data stream uses high percentage of network bandwidth. This is because bandwidth required by video/audio applications can vary in very wide range when real-time data becomes majority network traffic, tha

Gait can reveal sleep quality with machine learning models

Sleep quality is an important health indicator, and the current measurements of sleep rely on questionnaires, polysomnography, etc., which are intrusive, expensive or time consuming. Therefore, a more nonintrusive, inexpensive and convenient method needs to be developed. Use of the Kinect sensor to capture one&#39;s gait pattern can reveal whether his/her sleep quality meets the requirements. Fifty-nine healthy students without disabilities were recruited as participants. The Pittsburgh Sleep Quality Index (PSQI) and Kinect sensors were used to acquire the sleep quality scores and gait data. After data preprocessing, gait features were extracted for training machine learning models that predicted sleep quality scores based on the data. The t-test indicated that the following joints had stronger weightings in the prediction: the Head, Spine Shoulder, Wrist Left, Hand Right, Thumb Left, Thumb Right, Hand Tip Left, Hip Left, and Foot Left. For sleep quality prediction, the best result was achieved by Gaussian processes, with a correlation of 0.78 (p &lt; 0.001). For the subscales, the best result was 0.51 for daytime dysfunction (p &lt; 0.001) by linear regression. Gait can reveal sleep quality quite well. This method is a good supplement to the existing methods in identifying sleep quality more ecologically and less intrusively.</p

Data from: Common genetic variation in ETV6 is associated with colorectal cancer susceptibility

Genome-wide association studies (GWAS) have identified multiple susceptibility loci for colorectal cancer, but much of heritability remains unexplained. To identify additional susceptibility loci for colorectal cancer, here we perform a GWAS in 1,023 cases and 1,306 controls and replicate the findings in seven independent samples from China, comprising 5,317 cases and 6,887 controls. We find a variant at 12p13.2 associated with colorectal cancer risk (rs2238126 in ETV6, P = 2.67 × 10-10). We replicate this association in an additional 1,046 cases and 1,076 controls of European ancestry (P = 0.034). The G allele of rs2238126 confers earlier age at onset of colorectal cancer (P = 1.98 × 10-6) and reduces the binding affinity of transcriptional enhancer MAX. The mRNA level of ETV6 is significantly lower in colorectal tumors than in paired normal tissues. Our findings highlight the potential importance of genetic variation in ETV6 conferring susceptibility to colorectal cancer