Web Data Extractor Pro 3.0 Crack [UPDATED]
Web Data Extractor Pro is a web scraping toolspecifically designed for mass-gathering of various data types. Itcan harvest URLs, phone and fax numbers, email addresses, as wellas meta tag information and body text. Special feature of WDE Prois custom extraction of structured data. This high-speed andmultithreaded program works by using a keyword into search engines,by spidering a website or a list of URLs from a file. You can alsoallow it to follow external links from the original pages, with thecapability to go as deep into the URL paths as you need andactually search the entire Internet.Web Data Extractor is superior for harvesting structuredinformation and specific data types related to the keywords youprovide by searching through multiple layers of websites.
web data extractor pro 3.0 crack
Type-1 IFNs require their cognate receptor (interferon alpha receptor (IFNAR)), comprised of IFNAR1 and IFNAR2 subunits to bind and signal via the Jak/Stat cascade . Furthermore, studies suggest that the IFNAR1 subunit is responsible for type-1 IFN subtype recognition and subsequent differential signalling [51,52]. Over-expression of the IFNAR1 subunit, increasing type-1 IFN signal transduction, resulted in M17 cells being more susceptible to OGD induced death. In contrast, knockdown of the IFNAR1 subunit reversed this detrimental phenotype with reduced pro-inflammatory cytokines and subsequent neuro-protection. Uncontrolled neuro-inflammation is able to facilitate cell death through multiple degenerative mechanisms including extrinsic and intrinsic apoptosis. Ablation of type-1 IFN signalling through removal of IFNAR1 resulted in reduced levels of pro-apoptotic cleaved caspase-3 in the OGD environment. These data suggest that removal of IFNAR1 confers protection through limiting apoptosis, pertinent as caspase-3 pathway inhibitors have been previously protective in stroke outcome . Thus, we propose that type-1 IFN signalling is detrimental in hypoxic-ischaemic injury and modulation of this signalling may be beneficial to injury outcome. This study demonstrates the net effect of removing type-1 IFN signalling, however considering the pleiotropic nature [54,55] of the type-1 IFNs and their potential for beneficial functions in neuro-inflammation, a subtype specific functional analysis should be considered. Furthermore, this study investigates knockdown of IFNAR1 in M17 neuroblastoma cells alone in the context of OGD. While this approach identifies a critical neuronal type-1 IFN response in OGD-induced neuro-degeneration, these cells are normally embedded in a complex matrix of glial cells and form a cohesive system. It will be intriguing to identify if this protective phenotype is maintained in the brain environment where the presence of astrocytes and microglia enhance the severity of the cytokine storm in conditions of hypoxia-ischaemia and, if type-1 IFN signalling is critical in all cell types or just neurons.
Social media networks provide an abundance of diverse information that can be leveraged for data-driven applications across various social and physical sciences. One opportunity to utilize such data exists in the public health domain, where data collection is often constrained by organizational funding and limited user adoption. Furthermore, the efficacy of health interventions is often based on self-reported data, which are not always reliable. Health-promotion strategies for communities facing multiple vulnerabilities, such as men who have sex with men, can benefit from an automated system that not only determines health behavior risk but also suggests appropriate intervention targets.
The Gay Social Networking Analysis Program was created as a preliminary framework for intelligent web-based health-promotion intervention. The program consisted of a data collection system that automatically gathered social media data, health questionnaires, and clinical results for sexually transmitted diseases and drug tests across 51 participants over 3 months. Machine learning techniques were utilized to assess the relationship between social media messages and participants' offline sexual health and substance use biological outcomes. The F1 score, a weighted average of precision and recall, was used to evaluate each algorithm. Natural language processing techniques were employed to create health behavior risk scores from participant messages.
Offline HIV, amphetamine, and methamphetamine use were correctly identified using only social media data, with machine learning models obtaining F1 scores of 82.6%, 85.9%, and 85.3%, respectively. Additionally, constructed risk scores were found to be reasonably comparable to risk scores adapted from the Center for Disease Control.
With more than 40% of health care consumers utilizing social media for their health-related decision making, social networks have indeed caught the attention of the public health domain . Population-based analyses and in-person interventions are costly, both in time and resources . Health spending is projected to grow at an average rate of 5.5% per year, totaling $6.0 trillion by 2027, nearly one-fifth of the United States gross domestic product . Given these costs, the opportunity for high user engagement, and accessibility of social media data, social networks provide a new opportunity to public health. In recent years, social media have been employed in behavioral and public health research and has demonstrated its effectiveness in prevention, education, and treatment [19,20]. For instance, analyzing user activity on social media platforms has been an effective way to estimate the risk and time of HIV infection . A separate study  found that the strength of associations in a social network, its network shape, and size are predictors of HIV and sexually transmitted infection risk.
In practice, adaptive intervention systems are driven by an ability to determine health risks. Placed in the context of social media data, it is encouraging to learn that assessing health risk using textual sources has shown promising results in disease-specific risk evaluation and in identifying individuals at higher risk of depression and self-harm [30-32]. While text collection from electronic health records is extremely effective in determining a diagnosis, it is not a readily available resource for continuous risk assessment. Meanwhile, social media text data have the advantage of being abundantly available and cost-effective. While these data are not as domain constrained as clinical notes, they remain promising channels to explore for risk assessment.
Additionally, system interventions should be able to accurately evaluate when an individual is about to engage in a targeted health risk behavior with high probability followed by successfully reducing such behaviors. Maher et al  reviewed the effectiveness of past social network interventions, concluding with a call for stronger evidence in interventions that incorporate online social networks. Our paper responds to this call by evaluating the efficacy of social media data in determining health risk behavior. We are guided by the following questions: (1) Can we further substantiate the association between online social networking technologies and offline sexual and substance use behaviors? (2) Can we extract health risk scores from social media data that align with public health expert evaluation?
In this paper, the practicality of social media as an intervention modality is evaluated through social media data to identify health risk behavior in a sample of men who have sex with men from Los Angeles, California. The contributions of this paper are the following: (1) an end-to-end platform that continuously collects data from common social media platforms and specialized social networks tailored to the men who have sex with men community, and in tandem, biological data and personal health questionnaires were collected at baseline, 1-month, and 3-months from intake; (2) health behavior risk scores that are comparable to adapted risk scores created by the Centers for Disease Control and Prevention (CDC) using natural language processing techniques; and (3) the application of machine learning techniques to determine the extent to which social media messages can be used to directly predict verified biological outcomes of substance use and sexual risk, reflected as sexually transmitted disease diagnoses.
Qualified participants were invited to an initial clinical visit to review the study in detail, ask for informed consent, and answer any of their remaining questions. Afterward, a series of lab tests were conducted to determine their substance use and the presence of sexually transmitted diseases. Site testing was conducted for Chlamydia trachomatis and Neisseria gonorrhoeae with pharyngeal, urethral, and anal swabs. Further tests included a rapid plasma reagin blood test for syphilis, a rapid oral test for HIV, and a urine drug screen. Additionally, a survey was completed by the participants which asked a series of questions regarding demographic characteristics, sexual risk behavior, illicit substance use, and online behavior. Finally, participants provided their log-in credentials for a set of social media sites on which they had been. The user credentials were registered in a custom data collection platform for each website and the participants authorized the data collection system to pull their daily online activity. We collected participant social media data for up to 3 months after onboarding. We found this to be a reasonable duration considering the need to follow participants long enough to observe any changes in social media use and behaviors over time that can be measured by follow-up surveys and their biomarkers.
The system began collecting participants' daily social media activity immediately after the baseline visit. One month into the study, they were scheduled to revisit the clinic and redo lab tests and surveys. A final follow-up was set for 3 months after the baseline visit to recollect lab and survey data in addition to conducting required off-boarding procedures, including the discontinuation of participant data collection. 350c69d7ab