An integrated strategy for quality control of Pseudobulbus Cremastrae seu Pleiones based on Q-marker.

Pseudobulbus Cremastrae seu Pleiones (PCsP), a traditional Chinese medicine known as ‶Shan-Ci-Gu″, possesses properties for clearing heat, counteracting toxicity, dissipating phlegm, and resolving masses. As a TCM with multiple bases, the dried pseudobulbs of Pleione bulbocodioides (PB), Pleione yunnanensis (PY) and Cremastra appendiculata (CA) are considered to be the official sources of PCsP. Additionally, several unofficial substitutes are also available in the market. To enhance the quality control of PCsP, an integrated strategy based on Q-marker was proposed. Initially, a study of integrating plant metabolomics, target isolation, structure identification, and activity testing afforded five Q-markers, including three new compounds. Furthermore, a quality evaluation method using a single standard to determine multi-components (SSDMC) based on Q-marker was established, which could effectively distinguish PB from CA and the counterfeit herbs. Finally, the transitivity of Q-markers was explored through a representative Chinese compound prescription containing PCsP. The results indicated that the identified Q-markers together with the established analysis methods could be effectively applied for quality control of PCsP and its preparations.

Constructing cropland ecological stability assessment method based on disturbance-resistance-response processes and classifying cropland ecological types.

The cropland ecosystem stability (CES) has received increasing attention, especially in ecologically fragile areas, because of its impact on cropland quality, agricultural production and its ability to resist external disturbances. In this study, we first introduced the concepts of resilience and resistance, proposed the ecosystem disturbance-resistance-response process, and established a framework for evaluating the spatial and temporal dynamics of the CES based on RS data, and innovatively combined the RS assessment results of CES with soil field samples data to further classify cropland ecological types (CET) in a key agricultural areas of the Qinghai-Tibetan Plateau, which can effectively identify those croplands in need of priority ecological protection. Results indicate that the combined interactions of disturbance, resistance and response systems affect CES, forming a complex process with significant fluctuations and spatial variations. We also conclude that the disturbance system is positively influenced by topography and precipitation, while slope negatively affects resistance system. Hydrothermal conditions positively influence resistance system, while the response system is influenced by environmental factors at a lower intensity in six periods. It was interesting to note that soil α-biodiversity indicators are significantly and positively correlated with CES at the end of the study period. Therefore, based on the CES assessment results, we further combined the soil α-biodiversity indicators to classify the type of spatial pattern of CET and found that the eastern and northern areas have better quality, which implied an increase in the CES and a higher level of soil biodiversity, which was ideal for cropland expansion. On the contrary, we concluded that the ecosystem maintenance of the Huangshui headwaters and the northern mountainous areas needs to be strengthened in order to reverse the ecological fragility here and safeguard the cropland productive capacity.

Research on a New Intelligent and Rapid Screening Method for Depression Risk in Young People Based on Eye Tracking Technology.

Depression is a prevalent mental disorder, with young people being particularly vulnerable to it. Therefore, we propose a new intelligent and rapid screening method for depression risk in young people based on eye tracking technology. We hypothesized that the "emotional perception of eye movement" could characterize defects in emotional perception, recognition, processing, and regulation in young people at high risk for depression. Based on this hypothesis, we designed the "eye movement emotional perception evaluation paradigm" and extracted digital biomarkers that could objectively and accurately evaluate "facial feature perception" and "facial emotional perception" characteristics of young people at high risk of depression. Using stepwise regression analysis, we identified seven digital biomarkers that could characterize emotional perception, recognition, processing, and regulation deficiencies in young people at high risk for depression. The combined effectiveness of an early warning can reach 0.974. Our proposed technique for rapid screening has significant advantages, including high speed, high early warning efficiency, low cost, and high intelligence. This new method provides a new approach to help effectively screen high-risk individuals for depression.

A new early warning method for mild cognitive impairment due to Alzheimer's disease based on dynamic evaluation of the "spatial executive process".

Objective: Mild cognitive impairment (MCI) due to Alzheimer's disease (AD), as an early stage of AD, is an important point for early warning of AD. Neuropathological studies have shown that AD pathology in pre-dementia patients involves the hippocampus and caudate nucleus, which are responsible for controlling cognitive mechanisms such as the spatial executive process (SEP). The aim of this study is to design a new method for early warning of MCI due to AD by dynamically evaluating SEP. Methods: We designed fingertip interaction handwriting digital evaluation paradigms and analyzed the dynamic trajectory of fingertip interaction and image data during "clock drawing" and "repetitive writing" tasks. Extracted fingertip interaction digital biomarkers were used to assess participants' SEP disorders, ultimately enabling intelligent diagnosis of MCI due to AD. A cross-sectional study demonstrated the predictive performance of this new method. Results: We enrolled 30 normal cognitive (NC) elderly and 30 MCI due to AD patients, and clinical research results showed that there may be neurobehavioral differences between the two groups in digital biomarkers captured during SEP. The early warning performance for MCI due to AD of this new method (areas under the curve (AUC) = 0.880) is better than that of the Minimum Mental State Examination (MMSE) neuropsychological scale (AUC = 0.856) assessed by physicians. Conclusion: Patients with MCI due to AD may have SEP disorders, and this new method based on dynamic evaluation of SEP will provide a novel human-computer interaction and intelligent early warning method for home and community screening of MCI due to AD.

Conservative management of multi-trauma induced peritonitis: Experience, outcomes, and indications.

BACKGROUND: The concept of mandatory laparotomy in treating traumatic peritonitis has been increasingly questioned recently. AIM: To summarize and share the experience of conservative treatment of patients with multi-trauma induced peritonitis. METHODS: A retrospective review was performed on patients with multiple injury induced traumatic peritonitis. RESULTS: A total of 184 patients with multiple injury induced traumatic peritonitis were reviewed. 46 of them underwent conservative treatment. None of the 46 patients with conservative treatment switched to surgical treatment, and all of them were cured and discharged after successful conservative treatment. No significant abnormal findings were observed at regular follow-up after discharge. CONCLUSION: Conservative management is safe, effective, feasible, and beneficial in hemodynamically stable patients with traumatic peritonitis if there is no definite evidence of severe abdominal visceral organ injury.

Thermally Reversible Cross-Linking of Recyclable Polyamide Materials Based on Schiff Base and Diels-Alder Reactions.

Recyclability of cross-link polymer materials is essential to alleviate environmental pollution caused by discarded or damaged polymers. Herein, a facile method for producing recyclable polyamide materials is developed. Linear polymer chains are constructed by Schiff base reaction between glutaraldehyde (GD) and furandiamine (FD). The linear polymer chains are crosslinked by bismaleimide (BM) to give rise to polyamide material, named GF-BMs. The resulting GF-BMs polyamide material possesses strong tensile strength (78 MPa) and good solvent resistance from room temperature to 135 °C. Especially, the thermally reversible Diels-Alder covalent bonds and dynamic imine bonds in the polymer network have a synergistic effect on fast-reprocessing, self-healing, and recyclability, which provides a new idea for recyclable materials.

A New Smart 2-Min Mobile Alerting Method for Mild Cognitive Impairment Due to Alzheimer's Disease in the Community.

The early identification of mild cognitive impairment (MCI) due to Alzheimer's disease (AD), in an early stage of AD can expand the AD warning window. We propose a new capability index evaluating the spatial execution process (SEP), which can dynamically evaluate the execution process in the space navigation task. The hypothesis is proposed that there are neurobehavioral differences between normal cognitive (NC) elderly and AD patients with MCI reflected in digital biomarkers captured during SEP. According to this, we designed a new smart 2-min mobile alerting method for MCI due to AD, for community screening. Two digital biomarkers, total mission execution distance (METRtotal) and execution distance above the transverse obstacle (EDabove), were selected by step-up regression analysis. For the participants with more than 9 years of education, the alerting efficiency of the combination of the two digital biomarkers for MCI due to AD could reach 0.83. This method has the advantages of fast speed, high alerting efficiency, low cost and high intelligence and thus has a high application value for community screening in developing countries. It also provides a new intelligent alerting approach based on the human-computer interaction (HCI) paradigm for MCI due to AD in community screening.

[Spatial and Temporal Distribution and Source Variation of Heavy Metals in Cultivated Land Soil of Xiangzhou District Based on EBK Interpolation Prediction and GDM Model].

In order to explore the spatial and temporal changes in spatial patterns and source changes in heavy metals in Xiangzhou District, 395 and 326 soil samples were collected from cultivated soil in Xiangzhou District in November 2009 and November 2019, respectively. The contents of Cr, Pb, As, Hg, and Cd during these two years were measured. The spatial pattern and variation distribution of five types of heavy metals during these two years were obtained by using the empirical Bayesian Kriging (EBK) method. The effect (q-statistic) of 19 environmental factors and 5 types of heavy metals was calculated by using the geographical detector model (GDM), and the changes over the two years were compared. The results showed that compared with that in 2009, the heavy metal contents of Cr, Pb, Hg, and As in Xiangzhou District were decreased as a whole in 2019, whereas the Cd content increased overall. The spatial differentiation of heavy metals in the soil in Xiangzhou District in 2019 was more complicated than that in 2009. Pb, Hg, and Cd in the south and Hg in the central urban area and surrounding areas also increased. The content of each element decreased to the north and northwest. Compared with that in 2009, the explanatory power of natural factors and the distance between pollution enterprises on the single factor of the five soil heavy metal contents in 2019 decreased, and the influence on the contents under the control of single factors decreased significantly. The superposition influence of human activity factors increased, especially the distance between residential land, road, and land for pollution enterprises and environmental factors on soil heavy metal elements. These results indicated that the changes in soil heavy metal sources in 2019 tended to be complex, with structural factors as the main influencing factor. The influence of the emission of polluting enterprises on heavy metal elements decreased, whereas the influence of human activities on heavy metal content increased.

[Effects of Spartina alterniflora invasion on the functional groups and niche of benthic crustaceans and fishes in mangrove wetland.] / äº’èŠ±ç±³è‰å ¥ä¾µå¯¹çº¢æ ‘æž—åº•æ –ç”²å£³åŠ¨ç‰©å’Œé±¼ç±»åŠŸèƒ½ç¾¤åŠç”Ÿæ€ä½çš„å½±å“.

To investigate the effects of Spartina alterniflora invasion on mangrove wetland ecosystem, the benthic crustacean and fish samples were collected using cage nets in Zhangjiang Estuary Mangrove Wetland, Fujian Pro-vince in August 2020, January and April 2021. The five sampling sites included two mangroves (Avicennia marina and Kandelia obovata) sites, two S. alterniflora sites, and one mudflat site. The abundance, biomass, index of relative importance (IRI), Shannon diversity index (H), Pielou evenness index (J), and Margalef richness index (D) were used to quantify the dominant species and species diversity of benthic crustaceans and fishes. The functional groups and niche of dominant species were also analyzed. A total of 37 species, from 2 phyla, 2 classes, 8 orders, and 17 families, were identified across the three seasons. Most of them were warm-water and euryhaline species, mainly carnivorous and omnivorous functional groups. The results of two-way analysis of variance and NMDS showed that the community diversity index of benthic crustacean and fish did not change significantly after the invasion of S. alterniflora compared with mudflat. Functional groups had changed significantly, with the species number of plankton functional groups, carnivorous and phytophagous functional groups being increased. Compared with mangrove sites, the number of species of benthic crustacean and fish in S. alterniflora sites was significantly increased. The spatio-temporal niche breadth of dominant species ranged from 0 to 1.4186, with Liza carinata (1.4186), Bostrychus sinensis (1.0168), Metapenaeus ensis (0.9469) and Exopalaemon carinicauda (0.8922) as the top four species.

High-Efficiency Parallel Cryptographic Accelerator for Real-Time Guaranteeing Dynamic Data Security in Embedded Systems.

Dynamic data security in embedded systems is raising more and more concerns in numerous safety-critical applications. In particular, the data exchanges in embedded Systems-on-Chip (SoCs) using main memory are exposing many security vulnerabilities to external attacks, which will cause confidential information leakages and program execution failures for SoCs at key points. Therefore, this paper presents a security SoC architecture with integrating a four-parallel Advanced Encryption Standard-Galois/Counter Mode (AES-GCM) cryptographic accelerator for achieving high-efficiency data processing to guarantee data exchange security between the SoC and main memory against bus monitoring, off-line analysis, and data tampering attacks. The architecture design has been implemented and verified on a Xilinx Virtex-5 Field Programmable Gate Array (FPGA) platform. Based on evaluation of the cryptographic accelerator in terms of performance overhead, security capability, processing efficiency, and resource consumption, experimental results show that the parallel cryptographic accelerator does not incur significant performance overhead on providing confidentiality and integrity protections for exchanged data; its average performance overhead reduces to as low as 2.65% on typical 8-KB I/D-Caches, and its data processing efficiency is around 3 times that of the pipelined AES-GCM construction. The reinforced SoC under the data tampering attacks and benchmark tests confirms the effectiveness against external physical attacks and satisfies a good trade-off between high-efficiency and hardware overhead.

Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods.

The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatality of individuals tested positive given the patient's underlying health conditions, age, sex, and other factors. As the allocation of resources toward a vulnerable patient could mean the difference between life and death, a fatality prediction model serves as a valuable tool to healthcare workers in prioritizing resources and hospital space. The models adopted were evaluated and refined using the metrics of accuracy, specificity, and sensitivity. After data preprocessing and training, our model is able to predict whether a covid-19 confirmed patient is likely to be dead or not, given their information and disposition. The metrics between the different models are compared. Results indicate that the deep learning model outperforms other machine learning models to solve this rare event prediction problem.