Effects of carbon source variability on enhanced Bio-hydrogen production.

This study investigated how glucose, starch, and rapeseed oil, three common food waste components with diverse molecular and physicochemical characteristics, influenced hydrogen production and microbial communities in dark fermentation under varying carbon/nitrogen (C/N) ratios. The results indicated that glucose and starch groups, significantly increased hydrogen yields to 235 mL H2/gVS (C/N = 40) and 234 mL H2/gVS (C/N = 40), respectively, while rapeseed oil, with a lower yield of 30 mL H2/gVS (C/N = 20), demonstrated a negative impact. Additionally, an accumulation of propionate was observed with increasing carbon source complexity, suggesting that simpler carbon sources favored hydrogen production and bacterial growth. Conversely, lipid-based materials required rigorous pre-treatment to mitigate their inhibitory effects on hydrogen generation. Overall, this study underscores the importance of carbon source selection, especially glucose and starch, for enhancing hydrogen production and microbial growth in dark fermentation, while highlighting the challenges posed by lipid-rich substrates that require intensive pre-treatment to optimize yields.

COVID-Dynamic: A large-scale longitudinal study of socioemotional and behavioral change across the pandemic.

The COVID-19 pandemic has caused enormous societal upheaval globally. In the US, beyond the devastating toll on life and health, it triggered an economic shock unseen since the great depression and laid bare preexisting societal inequities. The full impacts of these personal, social, economic, and public-health challenges will not be known for years. To minimize societal costs and ensure future preparedness, it is critical to record the psychological and social experiences of individuals during such periods of high societal volatility. Here, we introduce, describe, and assess the COVID-Dynamic dataset, a within-participant longitudinal study conducted from April 2020 through January 2021, that captures the COVID-19 pandemic experiences of >1000 US residents. Each of 16 timepoints combines standard psychological assessments with novel surveys of emotion, social/political/moral attitudes, COVID-19-related behaviors, tasks assessing implicit attitudes and social decision-making, and external data to contextualize participants' responses. This dataset is a resource for researchers interested in COVID-19-specific questions and basic psychological phenomena, as well as clinicians and policy-makers looking to mitigate the effects of future calamities.

Examining the utility of nonlinear machine learning approaches versus linear regression for predicting body image outcomes: The U.S. Body Project I.

Most body image studies assess only linear relations between predictors and outcome variables, relying on techniques such as multiple Linear Regression. These predictor variables are often validated multi-item measures that aggregate individual items into a single scale. The advent of machine learning has made it possible to apply Nonlinear Regression algorithms-such as Random Forest and Deep Neural Networks-to identify potentially complex linear and nonlinear connections between a multitude of predictors (e.g., all individual items from a scale) and outcome (output) variables. Using a national dataset, we tested the extent to which these techniques allowed us to explain a greater share of the variance in body-image outcomes (adjusted R2) than possible with Linear Regression. We examined how well the connections between body dissatisfaction and dieting behavior could be predicted from demographic factors and measures derived from objectification theory and the tripartite-influence model. In this particular case, although Random Forest analyses sometimes provided greater predictive power than Linear Regression models, the advantages were small. More generally, however, this paper demonstrates how body image researchers might harness the power of machine learning techniques to identify previously undiscovered relations among body image variables.

Data augmentation for deep-learning-based electroencephalography.

BACKGROUND: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)-increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc. NEW METHOD: We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected? RESULTS: DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively. COMPARING WITH EXISTING METHODS: Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8 % for recombination of segmentation and 36 % for noise addition and from 14 % for motor imagery to 56 % for mental workload-29 % on average. CONCLUSIONS: DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications-if adhering to our reporting guidelines-will facilitate more detailed analysis.

[An imaging study on effect of total artificial disc replacement on lumbar sagittal alignment].

OBJECTIVE: To investigate effect of Activ L total lumbar disc replacement on lumbar sagittal alignment. METHODS: The imaging data of patients with degenerative disc disease received Activ L total lumbar disc replacement at Department of Orthopedics, Beijing Chao-Yang Hospital, Capital Medical University from March 2009 to March 2013 were retrospectively analyzed. The average age was 45.6 years(range, 35-60 years)and the surgery levels were as follows: L3-4 2 cases, L4-5 15 cases, L5/S1 5 cases, L3-4+ L4-5 3 cases, L4-5+ L5/S1 7 cases. All patients were followed up for 15 to 63 months(average, 32 months). Radiographic parameters such as lumbar lordosis angle(LL), segment lordosis angle(SL) and sacral slope angle(SS) were recorded. All the radiographic parameters were compared using one-way ANOVA at different stage. Lumbar lordosis angle of the two-level was compared with the one of one-level by using independent sample t-test before and after the operation. A partial correction test was carried out to determine the corrections between the parameters preoperatively, one month after the operation and at final follow-up. RESULTS: One month after the operation, the lumbar lordosis angle decreased by an average of 1.8°, but there was no statistically significant(P>0.05). Compared with one month postoperation, the lumbar lordosis angle increased by an average of 6.8°(P<0.05), which also increased a lot compared with preoperation(P<0.05). The value of segment lordosis angle was rising up from preoperation to the final follow-up(P<0.05), so was the value of sacral slope angle, but there was no statistically significant between different stage(P>0.05). The lumbar lordosis angle showed no significant difference between double-level ones and single-level ones at different stage(P<0.05). The lumbar lordosis angle showed positive correlation with the sacral slope(P<0.001), however, the lumbar lordosis angle showed no corrected with the segment angle all the time(P>0.05). CONCLUSIONS: The total lumbar disc replacement with Activ L prosthesis had contributed to maintain and improve the lumbar alignment in the short and medium term. Double- or single-level total lumbar disc replacement had no significant effect on the value of lumbar lordosis angle. The lumbar lordosis angle showed positive correlation with the sacral slope all the time with no correlation between lumbar lordosis angle and sacral slope.