Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of Light-Quality and Dark-White Growth Light Shifts in Short-Term Light Acclimation of Photosynthesis in Arabidopsis

Photosynthesis needs to run efficiently under permanently changing illumination. To achieve this, highly dynamic acclimation processes optimize photosynthetic performance under a variety of rapidly changing light conditions. Such acclimation responses are acting by a complex interplay of reversible molecular changes in the photosynthetic antenna or photosystem assemblies which dissipate excess energy and balance uneven excitation between the two photosystems. This includes a number of non-photochemical quenching processes including state transitions and photosystem II remodeling. In the laboratory such processes are typically studied by selective illumination set-ups. Two set-ups known to be effective in a highly similar manner are (i) light quality shifts (inducing a preferential excitation of one photosystem over the other) or (ii) dark-light shifts (inducing a general off-on switch of the light harvesting machinery). Both set-ups result in similar effects on the plastoquinone redox state, but their equivalence in induction of photosynthetic acclimation responses remained still open. Here, we present a comparative study in which dark-light and light-quality shifts were applied to samples of the same growth batches of plants. Both illumination set-ups caused comparable effects on the phosphorylation of LHCII complexes and, hence, on the performance of state transitions, but generated different effects on the degree of state transitions and the formation of PSII super-complexes. The two light set-ups, thus, are not fully equivalent in their physiological effectiveness potentially leading to different conclusions in mechanistic models of photosynthetic acclimation. Studies on the regulation of photosynthetic light acclimation, therefore, requires to regard the respective illumination test set-up as a critical parameter that needs to be considered in the discussion of mechanistic and regulatory aspects in this subject.
Enhancing Robustness of Machine Learning Integration With Routine Laboratory Blood Tests to Predict Inpatient Mortality After Intracerebral Hemorrhage

Objective: The accurate evaluation of outcomes at a personalized level in patients with intracerebral hemorrhage (ICH) is critical clinical implications. This study aims to evaluate how machine learning integrates with routine laboratory tests and electronic health records (EHRs) data to predict inpatient mortality after ICH. Methods: In this machine learning-based prognostic study, we included 1,835 consecutive patients with acute ICH between October 2010 and December 2018. The model building process incorporated five pre-implant ICH score variables (clinical features) and 13 out of 59 available routine laboratory parameters. We assessed model performance according to a range of learning metrics, such as the mean area under the receiver operating characteristic curve [AUROC]. We also used the Shapley additive explanation algorithm to explain the prediction model. Results: Machine learning models using laboratory data achieved AUROCs of 0.71–0.82 in a split-by-year development/testing scheme. The non-linear eXtreme Gradient Boosting model yielded the highest prediction accuracy. In the held-out validation set of development cohort, the predictive model using comprehensive clinical and laboratory parameters outperformed those using clinical alone in predicting in-hospital mortality (AUROC [95% bootstrap confidence interval], 0.899 [0.897–0.901] vs. 0.875 [0.872–0.877]; P <0.001), with over 81% accuracy, sensitivity, and specificity. We observed similar performance in the testing set. Conclusions: Machine learning integrated with routine laboratory tests and EHRs could significantly promote the accuracy of inpatient ICH mortality prediction. This multidimensional composite prediction strategy might become an intelligent assistive prediction for ICH risk reclassification and offer an example for precision medicine.
Gabaergic Interneurons in Early Brain Development: Conducting and Orchestrated by Cortical Network Activity

Throughout early phases of brain development, the two main neural signaling mechanisms—excitation and inhibition—are dynamically sculpted in the neocortex to establish primary functions. Despite its relatively late formation and persistent developmental changes, the GABAergic system promotes the ordered shaping of neuronal circuits at the structural and functional levels. Within this frame, interneurons participate first in spontaneous and later in sensory-evoked activity patterns that precede cortical functions of the mature brain. Upon their subcortical generation, interneurons in the embryonic brain must first orderly migrate to and settle in respective target layers before they can actively engage in cortical network activity. During this process, changes at the molecular and synaptic level of interneurons allow not only their coordinated formation but also the pruning of connections as well as excitatory and inhibitory synapses. At the postsynaptic site, the shift of GABAergic signaling from an excitatory towards an inhibitory response is required to enable synchronization within cortical networks. Concomitantly, the progressive specification of different interneuron subtypes endows the neocortex with distinct local cortical circuits and region-specific modulation of neuronal firing. Finally, the apoptotic process further refines neuronal populations by constantly maintaining a controlled ratio of inhibitory and excitatory neurons. Interestingly, many of these fundamental and complex processes are influenced—if not directly controlled—by electrical activity. Interneurons on the subcellular, cellular, and network level are affected by high frequency patterns, such as spindle burst and gamma oscillations in rodents and delta brushes in humans. Conversely, the maturation of interneuron structure and function on each of these scales feeds back and contributes to the generation of cortical activity patterns that are essential for the proper peri- and postnatal development. Overall, a more precise description of the conducting role of interneurons in terms of how they contribute to specific activity patterns—as well as how specific activity patterns impinge on their maturation as orchestra members—will lead to a better understanding of the physiological and pathophysiological development and function of the nervous system.
Physical Responses of Pinctada fucata to Salinity Stress

This study was conducted to understand the changes of physiological and biochemical indexes of black and red shell Pinctada fucata under acute high and low salt stress. In this study, the salinity of 35‰ was used as the control, while the salinities of 20 and 50% salinity were used as the low and high salt treatment groups, respectively. The osmotic pressure (OSM) and ion concentration in the hemolymph, Na+-K+ -ATPase (NKA) activity and respiratory metabolism in gills, and antioxidant and immune (non) enzymes in the hepatopancreas of P. fucata with two shell colors were compared and analyzed at the time periods of 1.5 and 3 h post-salinity stress. The results showed that the OSM and inorganic ion (Na+, Ca2+, and Cl–) concentration in the hemolymph of the black and red P. fucata increased significantly with the increase of salinity after the time periods of 1.5 and 3 h. At 3 h, the black P. fucata NKA activity decreased significantly with the increase of salinity, while red P. fucata reached the highest value at high salinity. The succinate dehydrogenase (SDH) and lactate dehydrogenase (LDH) activities of red P. fucata showed U-shaped and inverted U-shaped distributions with the increase of salinity after 1.5 h, respectively. With the increase of salinity, the phenoloxidase (POX) activity of red and black P. fucata showed inverted U-shaped and U-shaped distributions, respectively. The contents of glutathione (GSH) and vitamin C (VC) in black P. fucata decreased significantly with the increase of salinity at 1.5 and 3 h. Red P. fucata GSH and VC reached their maximum value in the 1.5-h low salinity group and 3-h high salinity group. The vitamin E (VE) content in black P. fucata increased significantly with the increase of salinity at 1.5 h, and reached the maximum at 3 h in the control group. Red P. fucata VE reached the maximum at 1.5 and 3 h in the control group. The results obtained from the present study revealed that the sensitivity of P. fucata to salinity varied in shell color. Compared to black P. fucata, red P. fucata responds more quickly to sharp salinity changes, thereby reducing more likely damage. Compared with a high salt environment, P. fucata was more adaptable to the changes of acute low salt environment. The results obtained from the present study provide the physical references for subsequent selective breeding of this species.
Impact of Na/Al Ratio on the Extent of Alkali-Activation Reaction: Non-linearity and Diminishing Returns

To address the high CO2 footprint associated with cement production, many alternative, sustainable binders are now gaining worldwide attention–including alkali-activated materials. The alkali-activation reaction of metakaolin is a fairly complex process involving transformation of one amorphous reactant (precursor metakaolin) into another amorphous product or products (N-A-S-H gel and/or disordered zeolite type phases). In spite of this complexity, researchers in the past 2 decades have gained significant knowledge on the nature of this reaction at multiple scales. Understanding and developing a clear relationship between the alkalinity of the mix and the extent of reaction is of high interest for practical applications. However, detailed and thorough investigations on this important relationship are limited. Here, in this study, we address this gap by systematically investigating a series of alkali-activated materials samples with a wide range of Na/Al ratios (0.5–1.8) using seven different yet complementary analytical techniques (isothermal calorimetry, FTIR, XRD, TGA, NMR, and Raman imaging). Applied in tandem, these tools reveal a clear but non-linear relationship between the Na/Al ratio and the extent of alkali-activation reaction indicating diminishing returns at higher Na/Al ratios, where higher Na/Al ratios cause an increase in the degree of reaction until a certain point at which the increase in Na/Al ratio does not significantly affect the reaction kinetics, but may affect the gel polymerization. These findings could potentially aid decision making for commercial applications of AAMs where alkalinity of the mix is an important parameter for performance as well as safety.
Tetraspanins as Potential Modulators of Glutamatergic Synaptic Function

Tetraspanins (Tspans) comprise a membrane protein family structurally defined by four transmembrane domains and intracellular N and C termini that is found in almost all cell types and tissues of eukaryotes. Moreover, they are involved in a bewildering multitude of diverse biological processes such as cell adhesion, motility, protein trafficking, signaling, proliferation, and regulation of the immune system. Beside their physiological roles, they are linked to many pathophysiological phenomena, including tumor progression regulation, HIV-1 replication, diabetes, and hepatitis. Tetraspanins are involved in the formation of extensive protein networks, through interactions not only with themselves but also with numerous other specific proteins, including regulatory proteins in the central nervous system (CNS). Interestingly, recent studies showed that Tspan7 impacts dendritic spine formation, glutamatergic synaptic transmission and plasticity, and that Tspan6 is correlated with epilepsy and intellectual disability (formerly known as mental retardation), highlighting the importance of particular tetraspanins and their involvement in critical processes in the CNS. In this review, we summarize the current knowledge of tetraspanin functions in the brain, with a particular focus on their impact on glutamatergic neurotransmission. In addition, we compare available resolved structures of tetraspanin family members to those of auxiliary proteins of glutamate receptors that are known for their modulatory effects.
What Factors Predict the Adoption of Type 2 Diabetes Patients to Wearable Activity Trackers—Application of Diffusion of Innovation Theory

Background: Globally, diabetes has brought an enormous burden to public health resources, and the situation of disease burden caused by diabetes in China is especially severe. China is currently facing the dual threat of aging and diabetes, and wearable activity trackers could promote elderly diabetic patients' physical activity levels and help them to manage blood glucose control. Therefore, examining the influencing factors of elderly patients' adoption intention is critical as wearing adoption determines actual wearing behaviors. Objective: This study aims to explore the predicting factors of Chinese elderly type 2 diabetic patients' adoption intention to wearable activity trackers and their actual wearing behavior, using diffusion of innovation theory as the theoretical framework. We hope to provide insights into future interventions using wearable activity trackers as tools to improve the outcome of patients. Methods: Wearable activity trackers were freely distributed to type 2 diabetic patients in Beijing, China. A questionnaire survey was conducted to examine predicting factors of adoption intention after a week's try-on. Actual wearing behavior for 3-month was obtained from the exclusive cloud. Data were analyzed with structural equation modeling. Results: A total of 725 patients completed the questionnaire. Patients had a mean age of 60.3 ± 7.6 years old and the educational level was generally lower. The results indicated that observability was the primary influencing factor of patients' adoption intention (β = 0.775, P < 0.001). Relative advantage (β = 0.182, P = 0.014) and perceived social image (β = 0.080, P = 0.039) also had a positive influence while perceived risk (β = −0.148, P < 0.001) exerted a negative influence. In addition, results showed that the more intention led to the better actual wearing behavior (β = 0.127, P = 0.003). Observability (β = 0.103, P = 0.005), perceived ease (β = 0.085, P = 0.004), and relative advantage (β = 0.041, P = 0.009) also indirectly influenced the wearing behavior. Conclusion: The intentions of Chinese elderly type 2 diabetic patients to wearable activity trackers directly influenced the actual wearing behavior. In addition, their adoption intention to wearable activity trackers was mainly influenced by observability, perceived ease to use, relative advantage, perceived risk, and social image.
A Survey of Human Gait-Based Artificial Intelligence Applications

We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.
Prediction of Excretion of Manure and Nitrogen

A compilation of N balance data (n = 1801) was partitioned into four groups to define the mean excretion of manure and N and to develop empirical equations to estimate these excretions from Holstein herds. Mean excretion of manure for cows that averaged 29 kg/d of milk production was 3 kg/d per 1000 kg of body weight (BW) more than the value for dairy cows reported by the American Society of Agricultural Engineers; N excretion was 0.09 kg/d per 1000 kg of BW higher than the value reported by the American Society of Agricultural Engineers. Mean excretion of manure and N for cows that averaged 14 kg/d of milk production and that for nonlactating cows were substantially lower than the values reported by the American Society of Agricultural Engineers. Growing and replacement cattle excreted 10 kg/d per 1000 kg of BW more manure and 0.11 kg/d per 1000 kg of BW more N than was reported by the American Society for Agricultural Engineers for beef cattle. Estimation of manure and N excretion was more accurate than mean values when using regression equations that included variables for milk production, concentration of crude protein and neutral detergent fiber in the diet, BW, days in milk, and days of pregnancy. Equations that contained intake variables did not significantly affect predictions of manure and N excretion, and the use of such equations is discouraged unless dry matter intake is meas
Rapid Patient-Side Evaluation of Endothelial Glycocalyx Thickness in Healthy Sedated Cats Using GlycoCheck® Software

The endothelial glycocalyx (EG) determines transvascular fluid fluxes, and influences inflammation, coagulation, and capillary blood flow. The GlycoCheck® software calculates EG thickness using sidestream dark field videomicroscopy recordings. This method has not been evaluated for use in cats. The aim of the present study was to evaluate the use of GlycoCheck® for estimating EG thickness in healthy cats, and to investigate the variability of EG thickness in this population. One hundred and one healthy research-purposed cats were included in the study. The cats were sedated, and a handheld videomicroscope, connected to GlycoCheck® software, was used to evaluate the sublingual microvasculature. The parameters measured included perfused boundary region (PBR, an indirect measurement of EG thickness) in vessels between 5 and 25 μm in diameter, valid vessel density, percentage red blood cell filling, and median red blood cell column width. Heart rate, respiratory rate, pulse oximetry and oscillometric blood pressure readings were also recorded. There were 35 neutered male cats, 11 intact males, 38 neutered females, and 17 intact females. The average age was 63 months (range, 11–160 months). Tolerance intervals for PBR (vessel diameter 5–25 μm) were 1.89–3.00 μm (95% CI, lower limit 1.76–2.04, upper limit 2.83–3.13 μm); for valid vessel density were 73.33–333.33 μm/mm2 (95% CI, lower limit 77.00–99.33, upper limit 312.67–350.33 μm/mm2); for percentage red blood cell filling were 59.85–85.07% (95% CI, lower limit 58.97–63.33, upper limit 83.07–88.20 %); and for median red blood cell column width were 5.63–8.59 μm (95% CI, lower limit 5.28–6.07, upper limit 8.14–9.51 μm). There was a negative association between median red blood cell column width and body weight (p = 0.007). The median red blood cell column was significantly wider in intact females when compared to spayed females (p = 0.033). The GlycoCheck® analysis was easily performed in healthy sedated cats. Clinical variables did not have an effect on the EG thickness. These results suggest that this technique could be valuable for evaluation of the EG and microvascular parameters in cats.
Disturbance of Gut Bacteria and Metabolites Are Associated with Disease Severity and Predict Outcome of NMDAR Encephalitis: A Prospective Case–Control Study

Objective We aimed to investigate the associations between the intestinal microbiota, metabolites, cytokines, and clinical severity in anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis and to further determine the predictive value of the intestinal microbiota or metabolites in clinical prognosis. Methods In this prospective observational cohort study of 58 NMDAR encephalitis patients and 49 healthy controls, fecal microbiota, metabolites, and cytokines were quantified and characterized by16S rRNA gene sequencing, liquid chromatography–mass spectrometry, and the Luminex assay, respectively. Results There were marked variations in the gut microbiota composition and metabolites in critically ill patients. We identified 8 metabolite modules (mainly characterized by fatty acid, glycerophosphoethanolamines, and glycerophosphocholines) that were distinctly classified as negatively or positively associated with bacterial co-abundance groups (CAGs). These CAGs were mainly composed of Bacteroides, Eubacterium_hallii_group, Anaerostipes, Ruminococcus, Butyricicoccus, and Faecalibacterium, which were substantially altered in patients. In addition, these fecal and serum metabolic modules were further correlated with the serum cytokines. Additionally, the combination of clinical features, microbial marker (Granulicatella), and a panel of metabolic markers could further enhance the performance of prognosis discrimination significantly, which yielded an area under the receiver operating characteristic curve of (AUC) of 0.94 (95%CI = 0.7–0.9). Patients with low bacterial diversity are more likely to develop relapse than those with higher bacterial diversity (log-rank p = 0.04, HR = 2.7, 95%CI = 1.0–7.0). Interpretation The associations between the multi-omics data suggested that certain bacteria might affect the pathogenesis of NMDAR encephalitis by modulating the metabolic pathways of the host and affecting the production of pro-inflammatory cytokines. Furthermore, the disturbance of fecal bacteria may predict the long-term outcome and relapse in NMDAR encephalitis.
SARS-CoV-2 Variants, Vaccines, and Host Immunity

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new beta coronavirus that emerged at the end of 2019 in the Hubei province of China. SARS-CoV-2 causes coronavirus disease 2019 (COVID-19) and was declared a pandemic by the World Health Organization (WHO) on 11 March 2020. Herd or community immunity has been proposed as a strategy to protect the vulnerable, and can be established through immunity from past infection or vaccination. Whether SARS-CoV-2 infection results in the development of a reservoir of resilient memory cells is under investigation. Vaccines have been developed at an unprecedented rate and 7 408 870 760 vaccine doses have been administered worldwide. Recently emerged SARS-CoV-2 variants are more transmissible with a reduced sensitivity to immune mechanisms. This is due to the presence of amino acid substitutions in the spike protein, which confer a selective advantage. The emergence of variants therefore poses a risk for vaccine effectiveness and long-term immunity, and it is crucial therefore to determine the effectiveness of vaccines against currently circulating variants. Here we review both SARS-CoV-2-induced host immune activation and vaccine-induced immune responses, highlighting the responses of immune memory cells that are key indicators of host immunity. We further discuss how variants emerge and the currently circulating variants of concern (VOC), with particular focus on implications for vaccine effectiveness. Finally, we describe new antibody treatments and future vaccine approaches that will be important as we navigate through the COVID-19 pandemic.