Social media mental health analysis framework through applied computational approaches

Studies have shown that mental illness burdens not only public health and productivity but also established market economies throughout the world. However, mental disorders are difficult to diagnose and monitor through traditional methods, which heavily rely on interviews, questionnaires and surveys, resulting in high under-diagnosis and under-treatment rates. The increasing use of online social media, such as Facebook and Twitter, is now a common part of people’s everyday life. The continuous and real-time user-generated content often reflects feelings, opinions, social status and behaviours of individuals, creating an unprecedented wealth of person-specific information. With advances in data science, social media has already been increasingly employed in population health monitoring and more recently mental health applications to understand mental disorders as well as to develop online screening and intervention tools. However, existing research efforts are still in their infancy, primarily aimed at highlighting the potential of employing social media in mental health research. The majority of work is developed on ad hoc datasets and lacks a systematic research pipeline. [Continues.]</div

Tweeting Your Mental Health: an Exploration of Different Classifiers and Features with Emotional Signals in Identifying Mental Health Conditions

Applying simple natural language processing methods on social media data have shown to be able to reveal insights of specific mental disorders. However, few studies have employed fine-grained sentiment or emotion related analysis approaches in the detection of mental health conditions from social media messages. This work, for the first time, employed fine-grained emotions as features and examined five popular machine learning classifiers in the task of identifying users with self-reported mental health conditions (i.e. Bipolar, Depression, PTSD, and SAD) from the general public. We demonstrated that the support vector machines and the random forests classifiers with emotion-based features and combined features showed promising improvements to the performance on this task

What about mood swings? Identifying depression on Twitter with temporal measures of emotions

Depression is among the most commonly diagnosed mental disorders around the world. With the increasing popularity of online social network platforms and the advances in data science, more research efforts have been spent on understanding mental disorders through social media by analysing linguistic style, sentiment, online social networks and other activity traces. However, the role of basic emotions and their changes over time, have not yet been fully explored in extant work. In this paper, we proposed a novel approach for identifying users with or at risk of depression by incorporating measures of eight basic emotions as features from Twitter posts over time, including a temporal analysis of these features. The results showed that emotion-related expressions can reveal insights of individuals’ psychological states and emotions measured from such expressions show predictive power of identifying depression on Twitter. We also demonstrated that the changes in an individual’s emotions as measured over time bear additional information and can further improve the effectiveness of emotions as features, hence, improve the performance of our proposed model in this task

The investigation into the adsorption removal of ammonium by natural and modified zeolites: Kinetics, isotherms, and thermodynamics

The objectives of this study were to modify Chinese natural zeolite by NaCl and to investigate its suitability as a low-cost clay adsorbent to remove ammonium from aqueous solution. The effect of pH on ammonium removal was investigated by batch experiments. The findings indicated that pH has a significant effect on the removal of ammonium by M-Zeo and maximum adsorption occured at pH 8. Ion exchange dominated the ammonium adsorption process at neutral pH, with the order of exchange selectivity being Na+ &gt; Ca2+ &gt; K+ &gt; Mg2+. The Freundlich model provided a better description of the adsorption process than the Langmuir model. The maximum ammonium adsorption capacity was 17.83 mg/g for M-Zeo at 293K. Considering the adsorption isotherms and thermodynamic studies, the adsorption of ammonium by M-Zeo was endothermic and spontaneous chemisorption. Kinetic studies indicated that the adsorption of ammonium onto M-Zeo is well fitted by the pseudo-second-order kinetic model. Ea in the Arrhenius equation suggested the adsorption of ammonium on M-Zeo was a fast and diffusion-controlled process. The regeneration rate was 90.61% after 5 cycles. The removal of ammonium from real wastewater was carried out, and the removal efficiency was up to 99.13%. Thus, due to its cost-effectiveness and high adsorption capacity, M-Zeo has potential for use in ammonium removal from aqueous solutions.Keywords: zeolite, sodium chloride modified, adsorbent, regeneration, wastewate

Driving mechanism of consumer migration behavior under the COVID-19 pandemic

IntroductionChina is now in the post-period of COVID-19 epidemic prevention and control. While facing normalized epidemic prevention and control, consumers behavioral intention and decision-making will still be influenced by the epidemic's development and the implementation of specific epidemic prevention measures in the medium to long term. With the impact of external epidemic prevention environment and measures, consumers' channel behavior has changed. How to better promote channel integration by adopting consumers' channel migration behavior is important for channel coordination strategies.MethodsThis paper takes fresh product retailing under normal epidemic prevention and control as an example and examines the change in channel migration behavior. Based on the value-based adoption model (VAM), this paper discusses the influence of channel characteristics and channel switching costs on channel migration intention, the mediating effect of perceived value between various influencing factors and channel migration intention, and the moderating effect of channel switching cost on perceived value and channel migration intention. Thus, an empirical study was carried out with 292 samples to verify the hypotheses.ResultsThe results show that under normal epidemic prevention and control, the influencing factors in the VAM model have a significant impact on channel migration intention; perceived value plays a mediating role between various influencing factors and channel migration intention.DiscussionsThe COVID-19 pandemic has had a significant effect on daily life and purchasing behavior. In the context of this pandemic, we have confirmed that consumers will probably change to other retailers when the usefulness, entertainment, and cost meet their expectation for purchasing fresh products. Channel characteristics have versatile features, such as channel structure and supply chain mode, which affect consumer behaviors in different ways. The perceived value comes from expectations and experience. Retailers should try to keep their products fresh and provide consumers with a high-level shopping experience during sale

Systems-Mapping of Herbal Effects on Complex Diseases Using the Network-Perturbation Signatures

The herbs have proven to hold great potential to improve people's health and wellness during clinical practice over the past millennia. However, herbal medicine for the personalized treatment of disease is still under investigation owing to the complex multi-component interactions in herbs. To reveal the valuable insights for herbal synergistic therapy, we have chosen Traditional Chinese Medicine (TCM) as a case to illustrate the art and science behind the complicated multi-molecular, multi-genes interaction systems, and how the good practices of herbal combination therapy are applicable to personalized treatment. Here, we design system-wide interaction map strategy to provide a generic solution to establish the links between diseases and herbs based on comprehensive testing of molecular signatures in herb-disease pairs. Firstly, we integrated gene expression profiles from 189 diseases to characterize the disease-pathological feature. Then, we generated the perturbation signatures from the huge chemical informatics data and pharmacological data for each herb, which were represented the targets affected by the ingredients in the herb. So that we could assess the effects of herbs on the individual. Finally, we integrated the data of 189 diseases and 502 herbs, yielding the optimal herbal combinations for the diseases based on the strategy, and verifying the reliability of the strategy through the permutation testing and literature verification. Furthermore, we propose a novel formula as a candidate therapeutic drugs of rheumatoid arthritis and demonstrate its therapeutic mechanism through the systematic analysis of the influencing targets and biological processes. Overall, this computational method provides a systematic approach, which blended herbal medicine and omics data sets, allowing for the development of novel drug combinations for complex human diseases

Quantitative structural analysis of hemifacial microsomia mandibles in different age groups

IntroductionThis study aims to quantitively analyze mandibular ramus and body deformities, assessing the asymmetry and progression in different components.MethodsThis is a retrospective study on hemifacial microsomia children. They were divided into mild/severe groups by Pruzansky-Kaban classification and into three age groups (&lt;1 year,1–5 years, 6–12 years old). Linear and volumetric measurements of the ramus and the body were collected via their preoperative imaging data to compare between the different sides and severities, using independent and paired tests, respectively. The progression of asymmetry was assessed by changes in affected/contralateral ratios with age using multi-group comparisons.ResultsTwo hundred and ten unilateral cases were studied. Generally, the affected ramus and body were significantly smaller than those on the contralateral side. Linear measurements on the affected side were shorter in the severe group. Regarding affected/contralateral ratios, the body was less affected than the ramus. Progressively decreased affected/contralateral ratios of body length, dentate segment volume, and hemimandible volume were found.DiscussionThere were asymmetries in mandibular ramus and body regions, which involved the ramus more. A significant contribution to progressive asymmetry from the body suggests treatment focus in this region

Dielectric barrier discharge-based defect engineering method to assist flash sintering

Oxygen vacancy OV plays an important role in a flash sintering (FS) process. In defect engineering, the methods of creating oxygen vacancy defects include doping, heating, and etching, and all of them often have complex processes or equipment. In this study, we used dielectric barrier discharge (DBD) as a new defect engineering technology to increase oxygen vacancy concentrations of green billets with different ceramics (ZnO, TiO2, and 3 mol% yttria-stabilized zirconia (3YSZ)). With an alternating current (AC) power supply of 10 kHz, low-temperature plasma was generated, and a specimen could be treated in different atmospheres. The effect of the DBD treatment was influenced by atmosphere, treatment time, and voltage amplitude of the power supply. After the DBD treatment, the oxygen vacancy defect concentration in ZnO samples increased significantly, and a resistance test showed that conductivity of the samples increased by 2–3 orders of magnitude. Moreover, the onset electric field (E) of ZnO FS decreased from 5.17 to 0.86 kV/cm at room temperature (RT); while in the whole FS, the max power dissipation decreased from 563.17 to 27.94 W. The defect concentration and conductivity of the green billets for TiO2 and 3YSZ were also changed by the DBD, and then the FS process was modified. It is a new technology to treat the green billet of ceramics in very short time, applicable to other ceramics, and beneficial to regulate the FS process