Multiple Usage Research Articles

GraphPowerNet: Graph-based power consumption profiling for mobile phone applications

Mobile phone power analysis provides an approach to user behavior profiling. Existing work conducts the analysis on either a single module or total power consumption, and ignores the correlation between modules. However, such correlation is vital in capturing usage patterns. To this end, graph neural networks (GNNs) are leveraged to explore such relationships among different modules. Since GNNs rely on pre-defined graph structures for message propagation, they cannot be applied directly to the scenario where the graph structure needs to be discovered. To resolve this issue, an approach called GraphPowerNet is developed. Firstly, a pseudo label-acquisition mechanism is used to cluster power consumption data into multiple virtual usage patterns that serve as supervisory information. Next, a graph learning module is designed to automatically extract the correlations among mobile phone modules. Based on the learned graph and the power consumption data, a graph convolutional model is adopted to conduct graph classification. Finally, the power consumption data is profiled by both the classification result and the graph structure. Experimental results on real world data show that the F1-score of application usage behavior classification is higher than 95%. Moreover, interpretability analysis shows that the graph learned via GraphPowerNet has strong practical significance.

Molecularly imprinted nanoparticles doped graphene oxide based electrochemical platform for highly sensitive and selective detection of L-tyrosine

A highly sensitive and selective electrochemical sensor was developed using a surface modified glassy carbon electrode (GCE) through molecularly imprinted polymerization on the surface of vinyltrimethoxysilane (VTMS) coated magnetic nanoparticle (Fe3O4) decorated silver nanoparticles incorporated graphene oxide, GO (VTMS-Fe3O4/AgGO) for L- Tyrosine (Tyr) detection. A molecular imprinting technique based on free radical polymerization was applied to synthesize molecularly imprinted Methacrylic acid (MAA) and Acrylamide (AA) grafted VTMS-Fe3O4/AgGO polymer (MAA/AA-g- VTMS-Fe3O4/AgGO) designated as MIP and non-imprinted polymer (NIP). The structure and morphology of the prepared polymers were FTIR, XRD, FE-SEM and VSM. MIP and NIP were chosen for modifying the GCE surface by drop casting process to construct the sensors and their electrochemical properties were characterized via EIS and CV. Compared with NIP/GCE sensor, MIP /GCE sensor exhibits excellent sensing response towards Tyr with a wide linear range of 0.25 × 10−13 M to 0.10 × 10−3 M and the limit of detection and limit of quantification as 0.15 × 10−13 M and 0.50 × 10−13 M, respectively with R2 value of 0.9934 by DPV technique. Moreover, MIP/GCE sensor exhibits long-time storage, excellent selectivity and good stability in multiple cycle usage. The practical applicability of MIP/GCE sensor was tested in human blood serum sample. The recovery percentage was obtained between 98.8% and 106.0% with a relative standard deviation (RSD) between 1.01% and 1.59%. Results of the investigations revealed the clinical applicability of the MIP/GCE sensor.

The Clinical Efficacy of Multidose Oritavancin: A Systematic Review.

Oritavancin (ORI) is a semisynthetic lipoglycopeptide approved as a single 1200 mg dose intravenous infusion for the treatment of acute bacterial skin and skin structure infections (ABSSSIs) caused by Gram-positive organisms in adults. The pharmacokinetic/pharmacodynamic (PK/PD) linear kinetic profile and long terminal half-life (~393 h) of ORI make it therapeutically attractive for the treatment of other Gram-positive infections for which prolonged therapy is needed. Multidose regimens are adopted in real-world clinical practice with promising results, but aggregated efficacy data are still lacking. A comprehensive search on PubMed/Medline, Scopus, Cochrane and Google Scholar databases was performed to include papers published up to the end of January 2023. All articles on ORI multiple doses usage, including case reports, with quantitative data and relevant clinical information were included. Two reviewers independently assessed papers against the inclusion/exclusion criteria and for methodological quality. Differences in opinion were adjudicated by a third party. From 1751 potentially relevant papers identified by this search, a total of 16 studies met the inclusion criteria and were processed further in the final data analysis. We extracted data concerning clinical response, bacteriologic response, mortality and adverse events (AEs). From the 16 included papers, 301 cases of treatment with multidose ORIs were identified. Multidose regimens comprised an initial ORI dose of 1200 mg followed by 1200 mg or 800 mg subsequent doses with a varying total number and frequency of reinfusions. The most often treated infections and isolates were osteomyelitis (148; 54.4%), ABSSSI (35; 12.9%) and cellulitis (14; 5.1%); and MRSA (121), MSSA (66), CoNS (17), E. faecalis (13) and E. faecium (12), respectively. Clinical cure and improvement by multidose ORI regimens were observed in 85% (231/272) and 8% (22/272) patients, respectively. Multidose ORI was safe and well tolerated; the most frequent AEs were infusion-related reactions and hypoglycemia. A multidose ORI regimen may be beneficial in treating other Gram-positive infections besides ABSSSIs, with a good safety profile. Further studies are warranted to ascertain the superiority of one multidose ORI scheme or posology over the other.

Identifying Social Determinants and Measuring Socioeconomic Inequalities in the Use of Four Different Mental Health Services by Australian Adolescents Aged 13-17 Years: Results from a Nationwide Study.

In this study, we aimed to identify the determinants of four different forms of mental health service usage (general health services, school counselling, telephone, and online services), and the number of mental health services accessed (single and multiple) by Australian adolescents aged 13-17 years. We also measured socioeconomic inequality in mental health services' usage following the concentration index approach within the same sample. The data came from the nationwide cross-sectional survey, Young Minds Matter (YMM): the second Australian Child and Adolescent Survey of Mental Health and Wellbeing. Random effect models were used to identify the factors associated with four different mental health services and the number of services accessed. Further, the Erreygers' corrected concentration indices for binary variables were used to quantify the socioeconomic inequality in each mental health service. The four services were the general health service (GP, specialist, psychiatrist, psychologist, hospital including emergency), school services, telephone counselling and online services. Overall, 31.9% of the total analytical sample (n = 2268) aged 13-17 years old visited at least one service, with 21.9% accessing a single service and 10% accessing multiple services. The highest percentage of adolescents used online services (20.1%), followed by general mental health services (18.3%), while school services (2.4%) were the least used service. Age, gender, family type and family cohesion statistically significantly increased the use of general health and multiple mental health service usage (p < 0.05). Area of residence was also found to be a significant factor for online service use. The concentration indices (CIs) were -0.073 (p < 0.001) and -0.032 (p < 0.001) for health and telephone services, respectively, which implies pro-rich socio-economic inequality. Adolescents from low-income families frequently used general mental health services and telephone services compared to those who belonged to high-income families. The study concluded that if we want to increase adolescents' usage of mental health services, we need to tailor our approaches to their socioeconomic backgrounds. In addition, from a policy standpoint, a multi-sectoral strategy is needed to address the factors related to mental health services to reduce inequity in service utilisation.

Prediction of multi-sectoral longitudinal water withdrawals using hierarchical machine learning models

Abstract Accurate models of water withdrawal are crucial in anticipating the potential water use impacts of drought and climate change. Machine learning methods can simulate the complex, nonlinear relationship between water use and potential explanatory factors, but rarely incorporate the hierarchical nature of water use data. This work presents a novel approach for the prediction of water withdrawals across multiple usage sectors using an ensemble of models fit at different hierarchical levels. Models were fit at the facility and sectoral grouping levels, as well as across facility clusters defined by temporal water use characteristics. Using repeated holdout cross-validation and a dataset of over 300,000 observations of monthly water withdrawal across 1,509 facilities, it demonstrates that ensemble predictions led to statistically significant improvements in predictive performance in five of the eight sectors analyzed. The use of ensemble modeling resulted in lower predictive errors compared to facility models in 65% of facilities analyzed. The relative improvement gained by ensemble modeling was greatest for facilities with fewer observations and higher variance, indicating its potential value in predicting withdrawal for facilities with relatively short data records or data quality issues.

Treatment strategy changes for inflammatory bowel diseases in biologic era: results from a multicenter cohort in Japan, Far East 1000

Many molecular targeted agents, including biologics, have emerged for inflammatory bowel diseases (IBD), but their high prices have prevented their widespread use. This study aimed to reveal the changes in patient characteristics and the therapeutic strategies of IBD before and after the implementation of biologics in Japan, where the unique health insurance system allows patients with IBD and physicians to select drugs with minimum patient expenses. The analysis was performed using a prospective cohort, including IBD expert and nonexpert hospitals in Japan. In this study, patients were classified into two groups according to the year of diagnosis based on infliximab implementation as the prebiologic and biologic era groups. The characteristics of therapeutic strategies in both groups were evaluated using association analysis. This study analyzed 542 ulcerative colitis (UC) and 186 Crohn’s disease (CD). The biologic era included 53.3% of patients with UC and 76.2% with CD, respectively. The age of UC (33.9 years vs. 38.8 years, P < 0.001) or CD diagnosis (24.3 years vs. 31.9 years, P < 0.001) was significantly higher in the biologic era group. The association analysis of patients with multiple drug usage histories revealed that patients in the prebiologic era group selected anti-tumor necrosis factor (TNF)-α agents, whereas those in the biologic era group preferred biologic agents with different mechanisms other than anti-TNF-α. In conclusion, this study demonstrated that both patient characteristics and treatment preferences in IBD have changed before and after biologic implementation.

Learning Dynamic App Usage Graph for Next Mobile App Recommendation

Next mobile app recommendation aims to recommend the next app that a user is most likely to use based on the user's app usage behaviors, which is beneficial for improving user experience, app pre-loading, and system optimization. However, existing works ignore the complex dependencies between apps in the app usage sessions. In addition, they do not consider the dynamics of user interests over time. To address these concerns, we propose a novel model named dynamic usage graph network (DUGN) to recommend the next app that a user is most likely to use. To model the complex dependencies among apps explicitly, we adopt the dynamic graph structure to learn the dynamics of user interests. Firstly, we extract user interests in each app usage graph by using the hierarchical graph attention mechanism. Secondly, we capture user interests evolving over time, and generate the dynamic user embeddings by modeling the temporal dependencies among multiple app usage graphs. Finally, we obtain the current user interests in the current app usage graph, fuse multiple user interests and generate comprehensive user embeddings for next mobile app recommendation. We conduct experiments on real-world datasets. The results show that our model outperforms the state-of-art recommendation methods.

SD^2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance.

Comprehensively evaluating and comparing researchers' academic performance is complicated due to the intrinsic complexity of scholarly data. Different scholarly evaluation tasks often require the publication and citation data to be investigated in various manners. In this paper, we present an interactive visualization framework, SD^2, to enable flexible data partition and composition to support various analysis requirements within a single system. SD^2 features the hierarchical histogram, a novel visual representation for flexibly slicing and dicing the data, allowing different aspects of scholarly performance to be studied and compared. We also leverage the state-of-the-art set visualization technique to select individual researchers or combine multiple scholars for comprehensive visual comparison. We conduct multiple rounds of expert evaluation to study the effectiveness and usability of SD^2 and revise the design and system implementation accordingly. The effectiveness of SD^2 is demonstrated via multiple usage scenarios with each aiming to answer a specific, commonly raised question.

Parkinson's Disease Case Ascertainment in the Sister Study: A Cohort for Environmental Health Research.

Large prospective studies are essential for investigating the environmental causes of Parkinson's disease (PD), but diagnosis via clinical exams is often infeasible. To present case ascertainment strategy and data collection in a US cohort of women. In the Sister Study (n = 50,884, baseline ages 55.6±9.0), physician-made PD diagnoses were first reported by participants or their proxies. Cohort-wide follow-up surveys collected data on subsequent diagnoses, medication usage and PD-relevant motor and nonmotor symptoms. We contacted self-reported PD cases and their treating physicians to obtain relevant diagnostic and treatment history. Diagnostic adjudication was made via expert review of all available data, except nonmotor symptoms. We examined associations of nonmotor symptoms with incident PD, using multivariable logistic regression models and reported odds ratio (OR) and 95% confidence intervals (CI). Of the 371 potential PD cases identified, 242 diagnoses were confirmed. Compared with unconfirmed cases, confirmed cases were more likely to report PD diagnosis from multiple sources, medication usage, and motor and nonmotor feature consistently during the follow-up. PD polygenic risk score was associated with confirmed PD (ORinter-quartile range = 1.74, 95% CI: 1.45-2.10), but not with unconfirmed cases (corresponding OR = 1.05). Hyposmia, dream-enacting behaviors, constipation, depression, unexplained weight loss, dry eyes, dry mouth, and fatigue were significantly related to PD risk, with ORs from 1.71 to 4.88. Only one of the eight negative control symptoms was associated with incident PD. Findings support our PD case ascertainment approach in this large cohort of women. PD prodromal presentation is likely beyond its well-documented profile.

Multiple usage of thorium-based fuel in a VVER-1000 reactor

This paper considers the use of unconventional fuel in nuclear power reactors, using the example of a VVER-type unit, in order to find out the possibility of saving natural fissile uranium nuclei. Saving fissile uranium is one of the important tasks, the solution of which will give time for the development of a two-component nuclear power industry that will have no problems with fuel resources. However, at present, the reserves of cheap uranium can provide the existing level of global nuclear energy for only 80–100 years. The main components of this proposed fuel are 232Th and fissile isotopes of uranium: 235U (loaded) and 233U (produced from thorium). All the uranium isotopes and added 235U nuclei at the beginning of the campaign account for about 6% of the number of thorium nuclei and uranium isotopes. The abbreviated name of this fuel is TORUR-5. To keep fissionable nuclei in the fuel cycle after the spent fuel is unloaded, it is envisaged that all the heavy nuclei will be returned back to the reactor after they have been cleaned from fission fragments, i.e., the fuel cycle will be closed. At the same time, the principle of annual movement of fuel assemblies (as they burn up) is the same as in the existing VVER-1000 reactors. Using the Serpent software, a reactor model was built, the composition and dimensions of which were close to the parameters of the VVER-1000 serial unit. The main results of calculations were the quantitative compositions of isotopes annually loaded into the reactor as well as the amounts of 235U and thorium added also annually. The analysis of the obtained results allowed us to make the following conclusions. The annual reloading of 235U during the computation period is required almost at a constant level and, in comparison with uranium fuel, is about half as much. This is feasible for the following reasons. Part of the fissions of 235U is replaced by the fission of 233U produced from 232Th. In addition, fissionable nuclei are kept in the closed Th-U fuel cycle. This is the first “advantage” of the proposed fuel. TORUR-5 requires uranium enriched to at least 90%, the cost of which is several times higher than that of 3–5% enriched uranium. But since much less highly enriched uranium is required, the cost of fuel for a TORUR-5-fueled VVER-1000 reactor is significantly lower. This is the second “advantage” of the proposed fuel. The negative characteristic of TORUR-5, which requires further investigation, is that, after the initial loading, several uranium isotopes appear in the returned fuel, the total radioactivity of which, according to estimates, exceeds the radioactivity of traditional 3–5% enriched uranium fuel by several thousand times. At the same time, the radioactivity of discharged spent conventional fuel exceeds the radioactivity of fresh fuel by millions of times, and this problem has been solved at NPPs both organizationally and technically. Therefore, it will be necessary to develop a technology for loading TORUR-5, taking into account the estimated radioactivity.

Factor Graph Framework for Smartphone Indoor Localization: Integrating Data-Driven PDR and Wi-Fi RTT/RSS Ranging

The classic fusion localization techniques based on the Kalman filter (KF) framework have been a focus of research community in the past decades, due to the limited computing power of the mobile devices. However, with computing-efficient and sensor-rich smartphones now being a commonplace, it is convenient and meaningful to provide more accurate positioning services for smartphones in an indoor environment. In this paper, we design and develop a tightly coupled fusion platform of Wi-Fi RTT, RSS, and data-driven PDR based on factor graph optimization for locating the consumer-grade smartphones in indoor environment. As compared to the existing PDR solutions, including step model-based approaches and data-driven approaches, the proposed PDR solution with magnetic information constraint can track the relative position change of pedestrians at 20 Hz, while supporting multiple smartphone usage poses. A comprehensive comparison between factor graph optimization (FGO), KF frame and its variants is also performed. The experimental results demonstrate that the proposed fusion platform achieves an average positioning accuracy of 0.39 m. In addition, it also improves the accuracy of EFK and ARKF by 45.83% and 27.78%, respectively. The analysis shows that as the smartphone computing performance continues to improve, the FGO-based sensor fusion gradually replaces the KF frame and its variants.

A Triad of Physical Masculinities: Examining Multiple ‘Hegemonic’ Bodybuilding Identities in Anabolic-Androgenic Steroid (AAS) Online Discussion Groups

ABSTRACT The last twenty years has seen increases in nonprofessional sportspersons using anabolic-androgenic steroids (AAS). Although some women use AAS, most users are men. Few studies examine men, masculinities and AAS. Of the limited studies available, many link a ‘hegemonic masculinity’ with men’s AAS use; adopting a singular identity perspective and generalizing ‘steroid use.’ However, bodybuilding is diverse, often encompassing different goals surrounding muscularity, size, weight and appearance – thus, bodybuilding identities are unlikely to be singular. This research questions narrow perspectives, developing an in-depth investigation that recognizes AAS use can be complex and depict multiple performance-enhancing drugs and usage routines. This study conducted a detailed thematic-content analysis of 351 user-postings from eight pro-AAS online discussion boards to examine linkages between men’s understandings of masculinity, male physicality, and how conceptualizations influence different trends for using AAS. Research applied R.W Connell’s hegemonic masculinity as a theoretical lens to interpret findings. Findings uncovered multiple and diverse notions of bodybuilding masculinity linked to three distinct notions of identity connecting different AAS practices (The Nattys, The Cyclers, and The Blasters-and-cruisers). An analysis of interactions between different bodybuilding identities is presented and discussed, alongside implications for masculinities theory and further study in this emerging but important field.

Ophthalmologic Examination and Echocardiography Should be the Essential Components of Candidemia Bundle.

Candidemia is the most common form of invasive candidiasis, and it is associated with end-organ involvement, prolonged hospitalization, increased mortality, and higher healthcare costs. Candidemia can lead to metastatic heart and ocular infections. This study aimed to define the incidence, characteristics, and mortality of candidemia episodes and compare the data with our center's previous results. In this single-center retrospective observational study, we enrolled 250 patients over 18 years diagnosed with candidemia between January 2015 and December 2020. We obtained patients' demographic, clinical, laboratory, and therapeutic data from medical records. An ophthalmologic examination and screening with echocardiography were carried out within the first week after candidemia diagnosis. There were 275 candidemia episodes from 250 patients. The incidence of candidemia was 2.8/1000 admissions and 5.68/ 10,000 inpatient days, higher than our previous results (1.23/1000 and 3.29/10,000). The median age was 65 (interquartile range [IQR]=52-75) years. Malignancies were the most frequent comorbidity (50%). The most common type was Candida albicans (n=115, 41.8%). Candida glabrata (n=61, 22.2%) was common, particularly in surgical patients, patients with malignancy, and critically ill patients. There was Infectious disease consultation in 93.3% (257) episodes.The ophthalmoscopic examination was made in 145 episodes (52.7%), and ophthalmitis was detected in 16 (11.0%). Echocardiography was performed in 139 (50.5%) episodes; one case had an endocarditis diagnosis. The 30-day mortality was 44.7% (n=123). Mortality rates in C. glabrata and Candida krusei infections were higher (54.1% and 66.7). The factors related to mortality were intensive care unit requirement (p=0.0001), chronic liver disease (p=0.005), corticosteroid usage (p=0.0001), previous antibiotic usage (p=0.013), multiple antibiotic usage ( p=0.020), and CVC related candidemia (p=0.010). Because of the life-threatening complications such as endocarditis, increased mortality rates, and higher healthcare costs, systematic and comprehensive candidemia bundle applications would be effective strategies for providing an effective antifungal stewardship program.

What Do I Suggest You Focus on in My Photo Story? The Effect of User Personality on the Position Significance of Jiugong Grid Images in Microblog.

Jiugong grid is one of the most used forms of multiple images posting in social media, with nine images arranged in three rows and three columns to present a related topic story from one microblog. The purpose of this study is to explore the relationship between the big five personality traits and the sequence position of Jiugong grid images. Two hundred thirty-seven volunteers completed a survey on the big five personality traits, and their 4,671 Jiugong grid microblogs with 42,039 photos were also obtained and analyzed. The results showed that users with varied kinds of personality traits could apply the significant position of Jiugong grid to emphasize certain content among multiple photos for a more attractable "story telling" in microblog. Compared with the image sequence position from the perspective of reading order, user personality traits had more relationship with that from the perspective of attention. This study is one of the first investigating the Jiugong grid image sharing behavior, which could theoretically enrich the social media image research from cognitive view and practically reveal the motivation of multiple images usage in social media, such as interface design and marketing purpose.

Frailty and its associates in community-dwelling older adults.

While the literature contains several studies on the frailty assessed during hospitalization and/or outpatient settings and nursing homes, few studies have assessed frailty in community-dwelling older adults. We investigated the prevalence of frailty and associated factors among older adults in a sample of community-dwelling older adults. We included community-dwelling older adults >60 years living in the Fatih District of the Istanbul Province. We conducted the study between November 2014 and May 2015. We collected the data such as age, sex, number of diseases and drugs, functional status, frailty, the presence of geriatric syndromes, common diseases, and quality-of-life assessment. Frailty was evaluated by the FRAIL scale. A total of 204 adults (mean age: 75.4±7.3 years) were included, of whom 30.4% were robust, 42.6% were pre-frail, and 27% were frail. In multivariate analyses, associated factors of frailty were the number of drugs [odds ratio (OR)=1.240, p=0.036], the presence of cognitive impairment (OR=0.300, p=0.016), and falls (OR=1.984, p=0.048). The present study established the prevalence of frailty in a large district in the largest metropolis in the country through a valid screening method. Our results suggest that clinicians should consider frailty evaluation in patients with multiple drug usage, cognitive impairment, and falls.

Multi-Life-Products – towards a new paradigm of product development aligning on module's multiple usage-scenarios

Successfully applying the principles of Circular Economy (CE) to products is predicted to save 53% of primary raw materials and 83% of CO2 emissions by 2050. Since 80% of the environmental impacts of a product result from its design, product design is one of the most decisive drivers for CE. This paper proposes the concept of "Multi-Life-Products" - a promising approach to designing products for a high level of circularity. It aims to maintain the value of obsolete products while saving energy and CO2 emissions. The "Multi-Life" concept intends to design products modularly so that their modules can be economically and ecologically applied in multiple usage scenarios (lives) after their first application's End of Life. As these modules are designed to be integrated into other technical systems after their life in the first application ends, multiple business cases can be built around them. "Multi-Life" is derived from the high-value strategies of reuse and repurpose, which are rethought in the context of product design. Different modules of a product may have different usage scenarios after their first application's End of Life. Products are thus to be understood as temporary configurations of modules, which will take different paths into different next-life applications. The potential of the concept is evaluated using existing performance criteria of CE and existing product examples. Some implications for the identification and design of "Multi-Life-Products" are discussed for further research work.

MICROBIOLOGICAL AND PHYSICOCHEMICAL ANALYSES OF STORED GROUND WATER IN THE EKEHUAN CAMPUS, UNIVERSITY OF BENIN, BENIN CITY

The culturable microbial and physicochemical quality of stored ground water supplied to three hostels, the health center and Optometry clinic at the Ekehuan campus of the University of Benin was evaluated. Routine procedures such as pour plate, multiple tube dilution and usage of appropriate meters such as pH meter and conductivity meter were employed. The mean bacterial and fungal counts ranged from 1.03×105 cfu/ml±1.2 to3.27×105 cfu/ml± 3.1 and 0.9 ×105 cfu/ml ± 2.6 to1.97×105 cfu/ml ± 0.6 respectively. Eight microbial isolates; Klebsiella mobilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Klebsiella sp., Aspergillus niger, Penicillium sp. and Rhizopus stolonifer were tentatively characterized. The mean pH values ranged from 7.36 ±0.4 to 8.67 ±0.1. It is recommended that the plastic storage tanks should be flushed and washed regularly. This measure would reduce or eradicate the biofilms associated with the interior surfaces of the plastic tanks

Implications of differential transcription start site selection on chronic myeloid leukemia and prostate cancer cell protein expression.

The relevance of minor transcription start sites in broad promoters is not well understood. We have studied AGAP2 expression in prostate cancer and chronic myeloid leukemia (CML), showing transcription is initiated from alternative transcription start sites (TSSs) within a single TSS cluster, producing cancer-type-specific AGAP2 mRNAs with small differences in their 5' UTR length. Interestingly, in the CML cell lines where the 5' UTR is longer, AGAP2 protein levels are lower. We demonstrate that the selection of an upstream TSS involved the formation of a G quadruplex in the 5' UTR, decreasing polysome formation. After developing a bioinformatics pipeline to query data from the FANTOM project and the NCl-60 human tumor cell lines screen, we found HK1 expression can also be regulated by the same mechanism. Overall, we present compelling data supporting TSS selection within a TSS cluster play a role in protein expression and should not be ignored.

The logic of recreation space governance in forest parks based on the theory of space production

Forest parks are among the most important reserves. Forest recreation reflects the time requirements for resource usage and economic coordination for sustainable development. Human activities in forest parks are a major force for continuing space production. However, with the rapid development of forest recreation, expanded spaces and excessive expectations make sustainable development a great challenge. Analyzing the spaces of forest recreation; making conclusions about the spatial features; and analyzing requests regarding multiple characters, multi-functions, and land usage are all done through the process of space production, which based on the “Three Dimensions.” According to the problems of space alienation occurring in forest recreation, this paper proposes that the logic of space governance should return to the nature of the Three Dimensions itself. “Space justice production” and “reconstruction of we” should also offer countermeasures and advice for building a classification mechanism for the rights, responsibilities, and interests of recreation spaces; establishing recreation space control zones and strategies; and promoting space expansion simulations and facility scale measurement. The construction of spatial relationships and forms of social, ecological, and natural justice is necessary to offer a new method of space production and consumption balance for natural reserves.

Drug Treatment Effect Model Based on MODWT and Hawkes Self-Exciting Point Process.

In precision medicine, especially in the pharmacodynamic area, the lack of an adequate long-term drug effect monitoring model leads to a quite low robustness to the instant drug treatment. Modelling the effect of drug based on the monitoring variables is essential to measure the drug benefit and its side effect preciously. In order to model the complex drug behavior in the context of time series, a sin function is selected to describe the basic trend of heart rate variable that is medically monitored. A Hawkes self-exciting point process model is chosen to describe the effect caused by multiple and sequential drug usage at different time points. The model considers the time lag between the drug given time and the drug effect during the whole drug emission period. A cumulative Gamma distribution is employed to describe the time lag effect. Simulation results demonstrate the established model effectively when describing the baseline trend and the drug effect with low noise levels, where the maximal overlap discrete wavelet transformation is utilized for the information decomposition in the frequency zone. The real data of the variables heart rate and drug liquemin from a medical database is analyzed. Instead of the original time series, scale variable s4 is selected according to the Granger cointegration test. The results show that the model accurately characterizes the cumulative drug effect with the Pearson correlation test value as 0.22, which is more significant for the value under 0.1. In the future, the model can be extended to more complicated scenarios through taking into account multiple monitoring variables and different kinds of drugs.