Miracle-dz/UniFAR-FTUs huggingface dataset:
Chemical, Antioxidant, and Antimicrobial Properties of the Peel and Male Flower By-Products of Four Varieties of Punica granatum L. Cultivated in the Marche Region for Their Use in Cosmetic Products

We are now seeing an increase in the production of agri-food waste, which is an essential resource for the recovery of bioactive compounds that may be employed as innovative natural ingredients in cosmetics. To date, the approach to cosmetics preservation has seen a significant shift in the search for biological components that give healthier alternatives for customers and help businesses operate in an environmentally friendly manner. To achieve this goal, we studied pomegranate extracts using the peel and, for the first time, extracts from the male flowers of a wide pomegranate variety cultivated in the Marche region, specifically, the Wonderful, Mollar de Elche, Parfianka, and less-studied G1 varieties. We studied the phenol compounds profile, antioxidant capacity, antimicrobial activity, and cell viability of the obtained pomegranate extracts. The identification and quantification of phenol compounds belonging to different classes, such as hydrolysable tannins, hydroxybenzoic acid, hydroxycinnamic acid, dihydroflavonol, gallocatechin, and anthocyanins, were performed using UPLC-ESI-MS/MS. Punicalagin isomers and punicalin resulted in the most abundant polyphenols found in the peel and male flower extracts. Mollar de Elche 2020 peel extract revealed a high concentration of punicalagin A and B (7206.4 mg/kg and 5812.9), while the content of gallic acid revealed high results in the G1 and Parfianka varieties. All extracts were spectrophotometrically analysed to determine their total phenol content (TPC) using the Folin–Ciocalteu method and their antioxidant capacity (AC). In terms of the total phenol obtained by the Folin–Ciocalteu colorimetric method, Mollar de Elche 2020 extracts reported the highest TPC content of 12.341 µmol GAE/g. Results revealed that the Mollar de Elche and Wonderful 2020 peel extracts demonstrated the highest TPC and AC. Furthermore, AC results indicated that the peel extracts displayed higher AC than the male flower extract due to the high punicalagin content detected by UPLC analysis. The antimicrobial activity testing revealed that the Wonderful and G1 2020 peel extracts resulted active against Escherichia coli, while all extracts exhibited promising anticandidal activity. Additionally, the cytocompatibility was evaluated in keratinocytes HaCaT cells by testing concentrations of pomegranate extracts ranging from 0.15 to 5.00 mg/mL. Extracts were non-toxic for the cells in the tested concentration range. The acquired results may help exploit pomegranate agri-food waste products provided by the Marche region’s short supply chain for their use as an antimicrobial and antioxidant booster in the formulation of cosmetic products.
Health Workers Readiness for Implementation of a Mobile Pregnancy Monitoring System in Primary Health Care: A Cross-Sectional Study

Purpose: The study aims to describe factors related to the readiness of health workers with respect to implementation of a mobile Pregnancy Monitoring System in Primary Health Care (PHC) sites of the South Tangerang District in Banten Province, Indonesia using a socio-technical approach. Methods: A cross-sectional survey design was used among respondents who were involved during the antenatal care process. The participants (n=210) completed the questionnaire that measured information needs regarding the socio-technical aspect of readiness and factors affecting the readiness. The data was analysed using logistic regression analysis. Result: The findings of this study showed that the majority of the health workers who were involved in the antenatal care process were ready to implement the mobile pregnancy monitoring system. Having social media (P= .013) and willingness (P= .007) to be involved in IT implementation are associated with IT readiness, while there is no significant association between demographic factors to the readiness of the health workers. Conclusion: Since there is no connectivity between the demographic factors to readiness, thus the supportive factors such as having a social media and willingness are easier to resolve than inferring from the demographic factors.
Salvia miltiorrhiza Bunge as a Potential Natural Compound against COVID-19

Salvia miltiorrhiza Bunge, commonly called danshen, is widely used in traditional Chinese medicine for its cardiovascular and neuroprotective effects, which include antioxidative, anti-inflammatory, and antifibrotic properties. The purpose of this study was to evaluate the preclinical potential of S. miltiorrhiza extracts for the treatment of COVID-19. First, the impact of the extract on the binding between SARS-CoV-2 and the cellular ACE2 receptors was assessed using atomic force microscopy (AFM), showing a significant reduction in binding by the extract at concentrations in the µg/mL range. Second, the interference of this extract with the inflammatory response of blood mononuclear cells (PBMCs) was determined, demonstrating potent inhibitory properties in the same concentration range on pro-inflammatory cytokine release and interference with the activation of NFκB signaling. Together, these in vitro data demonstrate the potential of S. miltiorrhiza against COVID-19, consisting first of the blockade of the binding of SARS-CoV-2 to the ACE2 receptor and the mitigation of the inflammatory response from leukocytes by interfering with NFκB signaling. This dataset prompts the launch of a clinical trial to address in vivo the clinical benefits of this promising agent.
The Impact of Model Size on the Sensitivity of Fit Measures in Measurement Invariance Testing

Abstract Model size has been shown to influence model fit measures in structural equation modeling, whereas its impact on measurement invariance testing using recommended cutoff criteria of fit measures is not clear. This study examined the impact of model size on the sensitivity of fit measures (CFI, RMSEA, AIC, BIC, SaBIC, LRT) to measurement noninvariance. Results suggested that CFI and RMSEA failed to detect metric noninvariance when model size was large (e.g., 6 factors with 8 items per factor). AIC and LRT were minimally affected by model size. Recommendations for empirical researchers were provided based on the findings of this simulation study.
A Hybrid Indoor Positioning Model for Critical Situations Based on Localization Technologies

The domains of positioning and tracking have undergone substantial evolution and advancements recently, especially within the concept of the Internet of Things (IoT) and in health care. Unfortunately, neither the current satellite positioning systems nor the standalone cellular systems remain useful for successfully localizing and tracking inside buildings. This paper proposes a new model that could improve the accuracy of localization in indoor environments. In addition, a broad review is conducted to discover the state-of-the-art indoor localization technologies appropriate for disasters and rescue situations. After a comprehensive study, three important technologies that need to be deeply reviewed are identified, which are wireless local area network (WLAN), dead reckoning (DR), and hybrid approaches. Based on these, a novel architecture is introduced that is more convenient to meet the operation of rescuing injured or older people in critical situations, where other technologies might be unavailable or require some extra infrastructures. The proposed model has two modes and selects one of these modes automatically. The first mode assumes the existence of both WLAN signals and smartphone sensors to be used for identifying the position of the object; otherwise, only smartphone sensors will be employed to achieve positioning. Significantly, the designated components and the flow control depicted provide a proper and suitable horizon for the next researchers who desire to develop a new indoor positioning system in this discipline with a low positioning root-mean-squared error on the centimeter scale that can later be incorporated in numerous applications relating to the IoT, health care, and evacuation plans.
MicroRNA-like snoRNA-Derived RNAs (sdRNAs) Promote Castration-Resistant Prostate Cancer

We have identified 38 specifically excised, differentially expressed snoRNA fragments (sdRNAs) in TCGA prostate cancer (PCa) patient samples as compared to normal prostate controls. SnoRNA-derived fragments sdRNA-D19b and -A24 emerged among the most differentially expressed and were selected for further experimentation. We found that the overexpression of either sdRNA significantly increased PC3 (a well-established model of castration-resistant prostate cancer (CRPC)) cell proliferation, and that sdRNA-D19b overexpression also markedly increased the rate of PC3 cell migration. In addition, both sdRNAs provided drug-specific resistances with sdRNA-D19b levels correlating with paclitaxel resistance and sdRNA-24A conferring dasatinib resistance. In silico and in vitro analyses revealed that two established PCa tumor suppressor genes, CD44 and CDK12, represent targets for sdRNA-D19b and sdRNA-A24, respectively. This outlines a biologically coherent mechanism by which sdRNAs downregulate tumor suppressors in AR-PCa to enhance proliferative and metastatic capabilities and to encourage chemotherapeutic resistance. Aggressive proliferation, rampant metastasis, and recalcitrance to chemotherapy are core characteristics of CRPC that synergize to produce a pathology that ranks second in cancer-related deaths for men. This study defines sdRNA-D19b and -A24 as contributors to AR-PCa, potentially providing novel biomarkers and therapeutic targets of use in PCa clinical intervention.
Psychosocial Factors and Psychological Characteristics of Personality of Patients with Chronic Diseases Using Artificial Intelligence Data Mining Technology and Wireless Network Cloud Service Platform

It was to explore the application value of health cloud service platform based on data mining algorithm and wireless network in the analysis of psychosocial factors and psychological characteristics of personality of patients with chronic diseases. Based on the demand analysis of cloud service platform for chronic diseases, a health cloud service platform including three modules was established: support layer, application layer, and interaction layer; and K-means algorithm and Apriori algorithm were used to mine and process data. The changes of pulse wave and EEG signal of epileptic seizures before and after processing by wireless network health cloud service platform were analyzed. 42 patients with idiopathic generalized epilepsy were selected as the research subjects, and 40 volunteers with normal physical examination during the same period were selected as the control group. The differences in the basic clinical characteristics data, Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Symptom Checklist 90 (SCL-90), and Eysenck Personality Questionnaire-Revision Short Scale for Chinese (EPQ-RSC) were compared between the two groups. It was found that the initial EEG signals of epileptic patients had noise pollution before and after the seizure, and the noise in the EEG signals was filtered out after digital technology processing in the cloud service platform. The maximum number of epileptic patients aged 18∼30 years was 17 (40.48%), and the mean scores of HAMD and HAMA scales in the epileptic group were significantly higher than those in the control group (P < 0.001). The total score of SCL-90, somatization, obsessive-compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychosis in the epilepsy group were obviously higher than those in the control group (P < 0.01). The mean value of EPQ-RSC and neuroticism (N) was clearly higher (P < 0.05), the mean value of extroversion (E) was significantly lower (P < 0.01), and the mean value of Lie Scale was significantly higher (P < 0.05) in the epileptic group in contrast with those in the control group. It indicates that the cloud service platform for chronic diseases based on artificial intelligence data mining technology and wireless network has potential application value. Epilepsy patients with chronic diseases should be paid more attention to their psychosocial factors and psychological characteristics of personality in the treatment process.
Application of Nanoliposomes Containing Nisin and Crocin in Milk

Purpose: This study aimed to investigate the effects of nanoliposomes containing crocin and nisin in milk samples as a food model. Therefore, three formulations were prepared and compared, including (1) milk samples containing free nisin and crocin, (2) samples with nanoliposomes containing nisin and crocin, and (3) nisin and crocin-loaded nanoliposomes coated with chitosan. Methods: In order to find the optimum amount of both bioactives within nanoliposomes, analyses of size, polydispersity index (PDI), zeta potential, and encapsulation efficiency were accomplished. Then, the best formulated nanoliposome was evaluated and compared with a solution containing free bioactives and nanoliposomes coated with chitosan using other experiments, including antioxidant and antibacterial activities, viscosity, colorimetric and bacterial growth. Results: The best nanoliposomal system based on the factors of size, PDI, zeta potential, and encapsulation efficiency was related for the nanocarrier with 4 mg crocin, 4.5 mg nisin, and 40 mg lecithin. Based on the results obtained, both nanoliposome (a*=5.41) and chitosancoated nanoliposome (a*=5.09) solutions could significantly (P<0.05) reduce the redness of milk induced by free bioactives (a*=12.32). However, viscosity of milk in chitosan-coated nanoliposome solution was found to be higher (3.42 cP) than other formulations (viscosity of samples with free bioactives was 1.65 cP and viscosity of samples containing nanoliposome was 1.71 cP). In addition, chitosan-coated nanoliposomes could inhibit the growth of Listeria monocytogenes stronger than other samples. Conclusion: Encapsulation of nisin and crocin in nanoliposomes showed promising results for preserving food safety and quality.
Analisis Kualitas Minyak Nilam (Pogostemon Cablin Benth) Produksi Kabupaten Buol

Patchouli ( pogostemon cablin benth ) is one of the essential oil producing plants which are quite important, known as Oil Patchouly. This oil pick strategic potential in the world market which is used as a binder fragrance in perfumes, cosmetics, pharmaceutical industry, and other industries. Buol which aims to determine the quality of patchouli oil. Samples is patchouli oil yield Buol. Parameters in meticulous ie specific gravity, refractive index, acid Numbers, Numbers Esther, and Solubility in Alcohol. The technique used is the technique of sampling area. The results showed that the Weighted Value Type 25oC / 25°C for samples A = 0.997 to 1.000, B = 0.962 to 0.965, C = 0.995 to 0.998 D = 0.980 to 0.983, and E = 0.968 - 0.971. To value Bias Index ( nD20 ) ie sample A = 1.45505 to 1.45705, B = 1.45506 to 1.45706, C = 1.45504 to 1.45704, D = 1.45503 to 1.45703, and E = 1.45523 to 1.45723. Numbers for acid sample value A = 2.805, B = 3.366, C = 2.5245, D = 3.0855 and E = 3.6465. For esther numbers are sample values A = 15.4275, B = 5.61, C = 12.6225, D = 4.2075 and E = 19.635. And last is the determination of solubility in alcohol that sample A = 1 : 2, B = 1 : 1.7, C = 1 : 0.9, D = 1 : 1, and E = 1 : 3. These data show patchouli oil production Buol area has good quality in terms of acid value, ester number and solubility in alcohol is the average Indonesian National Standards meet the standards and ISO. While the specific gravity and refractive index still does not meet the Indonesian National Standard and ISO.
Fusion of EEG-Based Activation, Spatial, and Connection Patterns for Fear Emotion Recognition

At present, emotion recognition based on electroencephalograms (EEGs) has attracted much more attention. Current studies of affective brain-computer interfaces (BCIs) focus on the recognition of happiness and sadness using brain activation patterns. Fear recognition involving brain activities in different spatial distributions and different brain functional networks has been scarcely investigated. In this study, we propose a multifeature fusion method combining energy activation, spatial distribution, and brain functional connection network (BFCN) features for fear emotion recognition. The affective brain pattern was identified by not only the power activation features of differential entropy (DE) but also the spatial distribution features of the common spatial pattern (CSP) and the EEG phase synchronization features of phase lock value (PLV). A total of 15 healthy subjects took part in the experiment, and the average accuracy rate was 85.00% ± 8.13%. The experimental results showed that the fear emotions of subjects were fully stimulated and effectively identified. The proposed fusion method on fear recognition was thus validated and is of great significance to the development of effective emotional BCI systems.
Theory of Particle Focusing in Inertial Microfluidic Devices

Author(s): Hood, Kaitlyn Tuley | Advisor(s): Roper, Marcus L | Abstract: Microfluidic devices are tiny circuits that flow fluids instead of electrons. Because they are inexpensive and portable, microfluidic devices are ideal for use in areas where medical resources are scarce. Inertial microfluidic devices represent a new direction in microfluidic device design in which high flow speeds are used to exert nonlinear inertial effects on the fluid and on fluid-suspended particles. While inertial microfluidic devices are finding applications in fields such as fluid mixing, particle filtration, flow cytometry (the counting, sorting, and analyzing of cells), the devices are built with essentially no theoretical input due to a lack of models for the nonlinear inertial effects.Why is there so little theory for inertial microfluidic devices? While there are many numerical methods for simulating inertial migration, because most devices have multiple moving boundaries and rely on three-dimensional effects, simulations are computationally intensive. In many cases, the computational time far exceeds the time needed to build and test a device experimentally. In contrast, asymptotic studies of inertial migration are only valid in limited cases, such as vanishingly small particle sizes.This thesis is concerned with developing a theory for inertial effects in microfluidic devices for a wide range of complicated geometries. This theory is achieved through the combination of both asymptotic and numerical methods. First, a theory is developed for the inertial lift force on a particle in a square channel. Second, this theory for the inertial lift force is validated against experiment. Third, a theory is developed for the formation of particle chains in a rectangular channel. Finally, a theory is developed for the number of focusing positions in a given channel.
Representations of people in urban building energy models

Occupant behaviour is commonly acknowledged as a key driver for variation in building energy performance (Gaetani et al., 2016). ASHRAE (2009) notes it as an important factor in the significant discrepancy between proposed building performance and actual energy consumption. A large body of literature exists dedicated to exploring energy behaviours and the need for more holistic considerations of energy behaviours, but this has not been connected to occupant modelling in Urban Building Energy Models (UBEMs). This paper develops a framework to identify and classify representations of people in UBEMs by reviewing and connecting the behaviour change and UBEM literatures. Combined with the classification of the approaches of people’s representation, we show that schedule-based models perform better although it cannot provide a full explanation of energy practices. While agent-based approaches offer the potential to incorporate the more holistic approaches called for by Kierstead (2006) the computational burdens which result may be excessive at the urban scale. The main framework developed can provide simulation practitioners with insights into energy behaviours.
The Predictive Value of Serum ACE2 and TMPRSS2 Concentrations in Patients with COVID-19—A Prospective Pilot Study

One of the major challenges for healthcare systems during the Coronavirus-2019 (COVID-19) pandemic was the inability to successfully predict which patients would require mechanical ventilation (MV). Angiotensin-Converting Enzyme 2 (ACE2) and TransMembrane Protease Serine S1 member 2 (TMPRSS2) are enzymes that play crucial roles in SARS-CoV-2 entry into human host cells. However, their predictive value as biomarkers for risk stratification for respiratory deterioration requiring MV has not yet been evaluated. We aimed to evaluate whether serum ACE2 and TMPRSS2 levels are associated with adverse outcomes in COVID-19, and specifically the need for MV. COVID-19 patients admitted to an Israeli tertiary medical center between March--November 2020, were included. Serum samples were obtained shortly after admission (day 0) and again following one week of admission (day 7). ACE2 and TMPRSS2 concentrations were measured with ELISA. Of 72 patients included, 30 (41.6%) ultimately required MV. Serum ACE2 concentrations >7.8 ng/mL at admission were significantly associated with the need for MV (p = 0.036), inotropic support, and renal replacement therapy. In multivariate logistic regression analysis, elevated ACE2 at admission was associated with the need for MV (OR = 7.49; p = 0.014). To conclude, elevated serum ACE2 concentration early in COVID-19 illness correlates with respiratory failure necessitating mechanical ventilation. We suggest that measuring serum ACE2 at admission may be useful for predicting the risk of severe disease.
Verification Upon Exposure to COVID-19 Misinformation: Predictors, Outcomes, and the Mediating Role of Verification

This study proposes a theory-oriented model that examines the predictors and outcomes of people’s verification of COVID-19 misinformation. Using an online experiment with 400 U.S. adults, this study showed that those who believed that others might be influenced by misinformation and that such influence had serious consequences for others as well as those with a higher level of fear and anxiety were more likely to perform institutional verification by using search engines, prestigious medical sites, or fact-checking sites. The intention to conduct institutional verification increased individuals’ efficacy beliefs regarding correcting misinformation, which motivated them to correct misinformation on social media.