Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass.

Hydrothermal liquefaction (HTL) is a thermochemical conversion technology that produces bio-oil from wet biomass without drying. However, by-product gases will inevitably be produced, and their formation is unclear. Therefore, an automated machine learning (AutoML) approach, automatically training without human intervention, was used to aid in predicting gaseous production and interpreting the formation mechanisms of four gases (CO2, CH4, CO, and H2). Specifically, four accurate optimal single-target models based on AutoML were developed with elemental compositions and HTL conditions as inputs for four gases. Herein, the gradient boosting machine (GBM) performed excellently with train R2 ≥ 0.99 and test R2 ≥ 0.80. Then, the screened GBM algorithm-based ML multi-target models (maximum average test R2 = 0.89 and RMSE = 0.39) were built to predict four gases simultaneously. Results indicated that biomass carbon, solid content, pressure, and biomass hydrogen were the top four factors for gas production from HTL of biomass. This study proposed an AutoML-aided prediction and interpretation framework, which could provide new insight for rapid prediction and revelation of gaseous compositions from the HTL process.

Prognostic values of regulatory T cells (Tregs) and Treg-related genes in gastric cancer.

Introduction: This study attempted to investigate the potential of a risk model constructed for regulatory T cells (Tregs) and their related genes in predicting gastric cancer (GC) prognosis. Material and methods: We used flow cytometry to detect the content of CD4+CD25+ Tregs. After detecting expression of five Treg-related genes by quantitative real-time polymerase chain reaction (qRT-PCR), Pearson analysis was employed to analyze the correlation between Tregs and related gene expression. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), colony formation and transwell assays were used to detect the effects of a disintegrin and metalloproteinase with thrombospondin motifs 12 (ADAMTS12) on cell functions. A prognostic risk model was built after Cox regression analysis. The Kaplan-Meier method was employed to assess how Tregs, 5-gene risk scores and expression of 5 genes were correlated with the survival time. Results: A significantly increased content of Tregs was found in GC tissues (p < 0.05). 5 Treg- related genes were significantly up-regulated in GC with a positive correlation with the content of Tregs (p < 0.05). Overexpression of ADAMTS12 significantly enhanced the viability, proliferation, migration and invasion of tumor cells. Kaplan-Meier analysis demonstrated poor overall survival and disease-free survival in the high-risk group. The results of survival analysis of Treg content and related gene expression were consistent with those of Cox analysis. Conclusions: The risk model constructed based on five Treg-related genes can enable effective prediction in the prognosis of GC patients.

Machine learning for hydrothermal treatment of biomass: A review.

Hydrothermal treatment (HTT) (i.e., hydrothermal carbonization, liquefaction, and gasification) is a promising technology for biomass valorization. However, diverse variables, including biomass compositions and hydrothermal processes parameters, have impeded in-depth mechanistic understanding on the reaction and engineering in HTT. Recently, machine learning (ML) has been widely employed to predict and optimize the production of biofuels, chemicals, and materials from HTT by feeding experimental data. This review comprehensively analyzed the application of ML for HTT of biomass and systematically illustrated basic ML procedure and descriptors for inputs and outputs of ML models (e.g., biomass compositions, operation conditions, yield and physicochemical properties of derived products) that could be applied in HTT. Moreover, this review summarized ML-aided HTT prediction of yield, compositions, and physicochemical properties of HTT hydrochar or biochar, bio-oil, syngas, and aqueous phase. Ultimately, future prospects were proposed to enhance predictive performance, mechanistic interpretation, process optimization, data sharing, and model application during ML-aided HTT.

Machine learning prediction of nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass.

Hydrothermal liquefaction (HTL) of high-moisture biomass or biowaste to produce bio-oil is a promising technology. However, nitrogen-heterocycles (NH) presence in bio-oil is a bottleneck to the upgrading and utilization of bio-oil. The present study applied the machine learning (ML) method (random forest) to predict and help control the bio-oil NH, bio-oil yield, and N content of bio-oil (N_oil). The results indicated that the predictive performance of the yield and N_oil were better than previous studies, achieving test R2 of 0.92 and 0.95, respectively. Acceptable predictive performance (test R2 of 0.82 and RMSE of 7.60) for the prediction of NH was also achieved. The feature importance analysis, partial dependence, and Shapely value were used to interpret the prediction models and study the NH formation mechanisms and behavior. Then, forward optimization of NH was implemented based on optimal predictive models, indicating the high potential of ML-aided bio-oil production and engineering.

A New Framework for Robust Heart Rate Measurement Based on the Head Motion State Estimation.

It is of great significance in managing human health, preventing and curing diseases such as heart disease to measure and monitor the physiological parameters accurately and robustly. However, imaging photoplethysmography (iPPG) can be easily affected by the ambient illumination variations or the subject's motions. In this paper, therefore, a novel framework of heart rate (HR) measurement robust to both illumination and motion artefacts is proposed, which combines the projection-plane-switching-based iPPG method (2PS) with the singular spectrum analysis (SSA). Based on the estimation of the head motion state, one reasonable projection plane is firstly determined, the temporally normalized red-green-blue signals are projected onto the plane and a pulse signal is obtained by alpha-tuning. After that, singular spectrum analysis (SSA) is applied to the obtained pulse signal and the normalized B-channel signal of the facial region of interest (ROI) to remove the artefacts remained in the pulse signal. For the self-collected database and the public PURE database, Bland-Altman plots show that the proposed 2PS-SSA has better agreement than the five compared methods, where the mean biases are 0.59 beat per minute (bpm) and 0.034 bpm, with 95% limits from -2.59 bpm to 3.78 bpm and from -1.97 bpm to 2.04 bpm, respectively.

Identification and Allelic Variants Associated With Cold Tolerance of PmPIAS in Pinctada fucata martensii.

The protein inhibitor of activated STAT (PIAS) functions in diverse aspects, including immune response, cell apoptosis, cell differentiation, and proliferation. In the present study, the PIAS in the pearl oyster Pinctada fucata martensii was characterized. The sequence features of PmPIAS were similar to that of other PIAS sequences with PIAS typical domains, including SAP, Pro-Ile-Asn-Ile-Thr (PINIT), RLD domain, AD, and S/T-rich region. Homologous analysis showed that PmPIAS protein sequence showed the conserved primary structure compared with other species. Ribbon representation of PIAS protein sequences also showed a conserved structure among species, and the PINIT domain and RLD domain showed the conserved structure compared with the sequence of Homo sapiens. The expression pattern of PmPIAS in different tissues showed significant high expression in the gonad. PmPIAS also exhibited a significantly higher expression in the 1 and 2 days after cold tolerance stress (17°C) and showed its potential in the cold tolerance. The SNP analysis of the exon region of PmPIAS obtained 18 SNPs, and among them, 11 SNPs showed significance among different genotypes and alleles between cold tolerance selection line and base stock, which showed their potential in the breeding for cold tolerance traits.

Research on the combination of color channels in heart rate measurement based on photoplethysmography imaging.

SIGNIFICANCE: The measurement of human vital signs based on photoplethysmography imaging (PPGI) can be severely affected by the interference of various factors in the measurement process; therefore, a lot of complex signal processing techniques are used to remove the influence of the interference. AIM: We comprehensively analyze several methods for color channel combination in the color spaces currently used in PPGI and determine the combination method that can improve the quality of the pulse signal, which results in a modified plane-orthogonal-to-skin based method (POS). APPROACH: Based on the analysis of the previous studies, 13 methods for color channel combination in the different color spaces, which can be seen as having potential abilities in measuring vital signs, were compared by employing the average value of signal-to-noise ratio (SNR) and the box-plot in the public databases UBFC-RPPG and PURE. In addition, the pulse signal was extracted through the dual-color space transformation (sRGB → intensity normalized RGB → YCbCr) and fine-tuning on the CbCr plane. RESULTS: Among the 13 methods for color channel combination, the signal extracted by the Cb+Cr combination in the YCbCr color space includes the most pulse information. Furthermore, the average SNR of the modified POS for all the used databases is improved by 69.3% compared to POS. CONCLUSIONS: The methods using prior knowledge are not only simple to calculate but can significantly increase the SNR, which will provide a great help in the practical use of vital sign measurements based on PPGI.

A measurement of illumination variation-resistant noncontact heart rate based on the combination of singular spectrum analysis and sub-band method.

BACKGROUND AND OBJECTIVE: The imaging photoplethysmography method is a non-contact and non-invasive measurement method that usually uses surrounding illumination as an illuminant, which can be easily influenced by the surrounding illumination variations. Thus, it has a practical value to develop an efficient method of heart rate measurement that can remove the interference of illumination variations robustly. METHOD: We propose a novel framework of heart rate measurement that is robust to illumination variations by combining singular spectrum analysis and sub-band method. At first, we extract the blood volume pulse signal by applying the modified sub-band method to the raw facial RGB trace signals. Then the spectra for the interference of illumination variations are extracted from the raw signal obtained from facial regions of interest by using singular spectrum analysis. Finally, we estimate the more robust heart rate through comparison analysis between the spectra of the extracted blood volume pulse signal and the illumination variations. RESULTS: We compared our method with several state-of-the-art methods through the analysis using the self-collected data and the UBFC-RPPG database. Bland-Altman plots and Pearson correlation coefficients pointed out that the proposed method could measure the heart rate more accurately than the state-of-the-art methods. For the self-collected data and the UBFC-RPPG database, Bland-Altman plots showed that proposed method caused better agreement with 95% limits from -4 bpm to 10 bpm and from -2 bpm to 4 bpm respectively than the other state-of-the-art methods. It also revealed that the highly linear relation was held between the estimated heart rate and ground truth with the correlation coefficients of 0.89 and 0.99, respectively. CONCLUSION: By extracting illumination variation directly from the facial region of interest rather than from the background region of interest, the proposed method demonstrates that it can overcome the drawbacks of the previous methods due to the illumination variation difference between the background and facial regions of interest. It can be found that the proposed method has a relatively good robustness regardless of whether illumination variation exists or not.

Systematic review of chinese traditional exercise baduanjin modulating the blood lipid metabolism.

Background. Baduanjin exercise is considered to be beneficial to modulate the blood lipid metabolism. The purpose of the systematic review was to assess the potential efficacy and safety of Baduanjin exercise. Methods. MEDLINE, EMBASE, CBM, CNKI, VIP, Chinese Important Conference Papers Database, and Chinese Dissertation Database were searched for all prospective-controlled trials of Baduanjin exercise from their inception to December 31, 2011. Results. A total of 14 studies were included. Comparing with no treatment, Baduanjin exercise significantly reduced the levels of TC, TG, LDL-C in plasma, and elevated plasma HDL-C level for healthy participants, and the pooled MD (95% confidence interval, CI) was -0.58 mmol/L (-0.86, -0.30 mmol/L), -0.22 mmol/L (-0.31, -0.13 mmol/L), -0.35 mmol/L (-0.54, -0.17 mmol/L), 0.13 mmol/L (0.06, 0.21 mmol/L), respectively. Baduanjin exercise also obviously decreased the levels of TG, LDL-C in plasma comparing with no treatment for patients, and the pooled MD (95% CI) was -0.30 mmol/L (-0.40, -0.19 mmol/L), -0.38 mmol/L (-0.63, -0.13 mmol/L), but there was not obvious to decrease plasma TC level or elevate plasma HDL-C level in patients with the pooled MD (95%CI), -0.39 mmol/L (-1.09, 0.31 mmol/L) and 0.22 mmol/L (-0.11, 0.55 mmol/L), respectively. In addition, the obvious advantage was not observed to modulate the blood lipid metabolism in comparing Baduanjin exercise with other exercises, regardless for health participants or patients. Conclusion. Studies indicated that Baduanjin exercise could significantly decrease the levels of TC, TG, LDL-C levels in plasma and elevate plasma HDL-C level for the healthy people. It also was helpful that Baduanjin exercise modulated the blood lipid metabolism for patients. Moreover, the Baduanjin exercise did not have an obvious advantage on modulating the lipid metabolism comparing with other exercises. But the evidence was uncertain because of the small sample size and low-methodological quality.