Related Products Research Articles

Do research universities specialize in disciplines where they hold a competitive advantage?

Enhancing the effectiveness and efficiency of national research systems is a top priority on the policy agendas of many countries. This study focuses on one aspect of the macroeconomic efficiency of research systems: whether research institutions specialize in scientific domains where they have a competitive advantage. To evaluate this, we developed a novel methodology. This methodology measures the scientific specialization indices of each organization in various research fields and assesses their relative research productivity. It then examines the correlation between these scores and between the resulting rankings. We applied this methodology to Italian universities. We found that a significant rank correlation between universities’ field specialization and their performance appears only in a few areas, and overall, the rankings are completely unrelated. Providing such data to research managers and policymakers can help inform strategies to enhance both micro- and macro-level efficiency.

Environmental factors affecting the distribution of deep-water rose shrimp (Parapenaeus longirostris, Lucas, 1846) abundance in the Strait of Sicily (Mediterranean Sea)

The distribution of deep-water rose shrimp (Parapenaeus longirostris, FAO 3 alpha code DPS), the main target species of demersal fisheries in the Strait of Sicily, is investigated in relation to surface parameters and biogeochemical processes. Such processes are known to influence sea bottom habitats and may be particularly relevant to the Strait of Sicily because of its relative shallowness and high surface primary production. Shrimp abundances recorded during multi-annual and seasonal trawl surveys (2004–2008) are analyzed. A GAMM and GAM model analysis is performed comparing juvenile abundances to monthly mean spatial patterns of remotely-sensed sea surface temperature (SST) and surface chlorophyll (chl), as well as their frontal structures, with a time-lag of one month, given the pelagic behavior of DPS early life stages preceding settlement. Juvenile and total shrimp abundances are also compared to the flux of particulate organic carbon (POC) to the seabed. The POC flux is computed via 1-D and 3-D models simulating sinking, re-mineralization and horizontal advection and diffusion of surface POC. The latter is derived from surface primary production maps obtained from ocean color data. Results show that the abundance of the juvenile fraction of DPS is significantly correlated with depth, distance to SST fronts and the intensity of chl fronts (correlation R2 = 80%). Furthermore, results strongly suggest the significant role of bottom POC flux in conditioning the distribution of DPS abundance, indicating that ecological processes occurring in surface waters influence food availability near the seabed in the investigated area.

Global Probabilistic Forecasting for Multiple Tight Gas Wells Using Deep Autoregressive Networks

Summary Traditional methods for forecasting production rate, such as Arps, analytical techniques, and recurrent neural network (RNN)–based deep learning, are mainly point prediction techniques developed within the framework of single-well forecasting. These methods often face limitations stemming from single-well historical production data and model assumptions, hindering their ability to consider the influence of development patterns of other production wells within the block on the target well. In addition, they struggle to predict the multiple production rate time series simultaneously and often fail to quantify uncertainty in predictions or adequately exploit extensive relevant historical production data. To tackle these challenges, we propose a model based on the deep autoregressive recurrent neural network (DeepAR), leveraging related multiwell production rate data to enable global modeling and probabilistic forecasting. This model incorporates dynamic covariate and static categorical variable data, integrating Bayesian inference and using gradient descent algorithms and maximum likelihood estimation methods to derive a comprehensive historical-future production probability evolution pattern shared across multiple wells. Leveraging data from 943 tight gas wells, a comprehensive evaluation of the DeepAR model’s performance was undertaken, encompassing the comparison of prediction accuracy with long short-term memory (LSTM), classification prediction, cold-start prediction, and single-well multitarget prediction scenarios, summarizing the applicability conditions for each. The research findings highlight that DeepAR integrates the acquired comprehensive production probability evolution pattern with specific production historical data of the target well to formulate a “comprehensive + specific” production probability prediction approach, resulting in improved stability and accuracy. On average, DeepAR demonstrates a 58.79% reduction in normalized deviation (ND) compared to the LSTM model, showcasing enhanced stability, particularly in scenarios involving frequent well shut-ins and openings. Moreover, DeepAR can learn static categorical features, with the classification model resulting in a 27.15% reduction in the ND compared to the unclassified model. Furthermore, DeepAR adeptly addresses the challenge of limited data availability, achieving cold-start prediction and facilitating multitarget single-well training and prediction while considering the interdependency among multiple variables over time and effectively mitigating the issue of missing auxiliary variables during the prediction phase. This study contributes to a broader understanding of production forecasting and analysis of production dynamics methods from a big data perspective.

Discrimination of coal geographical origins through HS-GC-IMS assisted with machine learning algorithms in larceny case

The process of globalization and industrialization has resulted in a rise in the theft of coal and other related products, thereby becoming a focal point for forensic science. This situation has engendered an escalated demand for effective detection and monitoring technologies. The precise identification of coal trace evidence presents a challenge with current methods, owing to its minute quantity, fine texture, and intricate composition. In this study, we integrated machine learning with the identification of volatiles to accurately differentiate coal geographical origins through the application of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). The topographic distribution of volatiles in coals was visually depicted to elucidate the subtle distinctions through spectra and fingerprint analysis. Additionally, four supervised machine learning algorithms were developed to quantitatively predict the geographical origins of natural coals utilizing the HS-GC-IMS dataset, and these were subsequently compared with unsupervised models. Remarkable volatile compounds were identified through the quantitative analysis and optimal Random Forest model, which offered a rapid readout and achieved an average accuracy of 100 % in coal identification. Our findings indicate that the integration of HS-GC-IMS and machine learning is anticipated to enhance the efficiency and accuracy of coal geographical traceability, thereby providing a foundation for litigation and trials.

Synthesis of Carbon Nitride Polymorphs by Sacrificial Template Method: Correlation between Physicochemical Properties and Photocatalytic Performance.

Carbon nitride compounds (CNCs) in the form of graphitic carbon nitride (g-C3N4) and poly(heptazine imide) were synthesized using different metal chloride salts (MClx), i.e., NaCl, KCl and CaCl2, as sacrificial templates and by varying the MClx to melamine molar ratios. A systematic study of their photocatalytic activity for H2 production in relation to the physicochemical, morphological, and optical properties was carried out. Each sample was tested achieving the highest hydrogen evolution rates of about 7660 µmol g-1 h-1, 5380 µmol g-1 h-1 and 3140 µmol g-1 h-1 using CaCl2, KCl, and NaCl, respectively. This work demonstrates how the synthesis of CNCs with different MClx leads to the production of high-performance photocatalysts due to a combination of factors as the formation of vacancies or cyano groups, a shift in the optical threshold and tuning of micro(nano)structure. The results demonstrate that, when CaCl2 is used as a sacrificial template, porous and exfoliated g-C3N4 nanosheets are formed leading to hydrogen productions which outperform most of the previously reported g-C3N4 prepared using a single synthetic step.

Cigarette Smoke-Induced Epithelial-to-Mesenchymal Transition: Insights into Cellular Mechanisms and Signaling Pathways.

This review delves into the molecular complexities underpinning the epithelial-to-mesenchymal transition (EMT) induced by cigarette smoke (CS) in human bronchial epithelial cells (HBECs). The complex interplay of pathways, including those related to WNT//β-catenin, TGF-β/SMAD, hypoxia, oxidative stress, PI3K/Akt, and NF-κB, plays a central role in mediating this transition. While these findings significantly broaden our understanding of CS-induced EMT, the research reviewed herein leans heavily on 2D cell cultures, highlighting a research gap. Furthermore, the review identifies a stark omission of genetic and epigenetic factors in recent studies. Despite these shortcomings, the findings furnish a consolidated foundation not only for the academic community but also for the broader scientific and industrial sectors, including large tobacco companies and manufacturers of related products, both highlighting areas of current understanding and identifying areas for deeper exploration. The synthesis herein aims to propel further research, hoping to unravel the complexities of the EMT in the context of CS exposure. This review not only expands our understanding of CS-induced EMT but also reveals critical limitations in current methodologies, primarily the reliance on 2D cell cultures, which may not adequately simulate more complex biological interactions. Additionally, it highlights a significant gap in the literature concerning the genetic and epigenetic factors involved in CS-induced EMT, suggesting an urgent need for comprehensive studies that incorporate these types of experiments.

Rapid mitragynine quantification and fingerprinting of products from Mitragyna speciosa Korth. leaf (Kratom) using high-performance thin-layer chromatography.

Kratom (leaves from Mitragyna speciosa Korth.; Rubiaceae) is a herbal medicine known for its analgesic properties and psychoactive effects. Kratom in Thailand is currently legal; however, it is prohibited in some countries and considered a narcotic plant. Our aim was to establish a reliable, simple, and rapid method for quantifying mitragynine in Kratom leaves and related products through a combination of high-performance thin-layer chromatography (HPTLC) and densitometry. A densitometric HPTLC method was developed and validated in terms of specificity, linearity, limit of detection (LOD), limit of quantification (LOQ), accuracy, precision, and robustness. The fingerprints of kratom leaves, Mitragyna spp., and related products were constructed. For HPTLC, samples were applied to silica gel 60 F254 plates, and the mobile phase comprised n-hexane, ethyl acetate, and triethylamine (1:1:0.15, v/v/v). Densitometric detection was carried out under ultraviolet light at a wavelength of 226 nm. The validated method exhibited a range of 14.31-143.10 μg/mL, yielding a correlation coefficient of 0.9993. Spiked recovery rates were within a range of 98.3%-100.9%, and the LOD and LOQ were 3.80 and 11.53 μg/mL, respectively. Kratom samples were analyzed with the developed method, and the correlation coefficient was 0.9641, compared to the high-performance liquid chromatography-diode-array detection (HPLC-DAD) method. The HPTLC fingerprints displayed a distinctive pattern, facilitating discrimination among different plant parts and Mitragyna spp. The established method offers the advantages of simplicity, ease of use, and speed of analysis, serving as a practical alternative for mitragynine quantification in kratom leaf and its related products.

Dynamics of productivity in pigeonpea [Cajanus cajan (L.) Millsp.] in subtropical Australia

AbstractPigeonpea productivity can be enhanced by optimally matching the physiology of genotypes to environmental conditions. Information on crop responses to the environment has been lacking for the short‐duration pigeonpea genotypes, which are being trialed to develop the Australian pigeonpea industry. The objective of this study was to examine the dynamics of productivity in relation to radiation use efficiency (RUE) and its influence on yield partitioning. Seven field trials, employing three pigeonpea [Cajanus cajan (L.). Millsp.] genotypes, were established at the Gatton Campus, the University of Queensland, Australia, in 2017/2018 and 2018/2019 summer seasons. The study reveals that leaf area development, influenced by growing environment, genotypes, and their interactions, were the key factors for the differences in leaf area duration and RUE. Pigeonpea planted in December had higher seasonal (1.11 g MJ−1) as well as reproductive (0.71 g MJ−1) RUE, resulting in significant differences in total dry matter (TDM) and grain yield (GY). GY was positively associated with seasonal RUE (R2 = 0.62), and the relationship was stronger (R2 = 0.83) for the reproductive phase (RUE(R)). The positive association between GY and RUE(R) suggested that maintaining optimum leaf area during the grain filling period is crucial to achieve higher productivity. Variations in GY were related to amount and rate of TDM accumulation before flowering (R2 = 0.51 and R2 = 0.53, respectively). Hence, achieving greater TDM before flowering was determinant for achieving higher productivity. The present study provided updated information on dynamics of productivity that will enable more comprehensive modelling of pigeonpea adaptation under subtropical conditions.

Neopterin production in relation to COVID-19 in the Haut-Ogooué Province, Gabon

BackgroundIn sub-Saharan Africa, understanding of the immune process associated with the COVID-19 pandemic remains scarce. This study aimed to investigate the relationship between plasma neopterin concentrations and COVID-19 infection, focusing on changes over time and age-related changes in immune response.MethodsA retrospective case study was conducted during the first wave of COVID-19 from March to August 2020. Whole blood and associated symptoms and comorbidities were collected from patients of all ages and sexes. Concentrations of plasma neopterin were measured using a commercial competitive neopterin ELISA (Neopterin ELISA, IBL International GmbH, Germany).ResultsWe analyzed data for 325 patients: 38% (n = 124) with COVID-19, and 62% (n = 201) without COVID-19, as a control group. We found that plasma neopterin concentrations were significantly higher in the COVID-19 group (mean value 45.1 nmol/L (SD 19)) than in the control group (mean value 33.8 nmol/L (SD 13)) (p = 0.004). In addition, neopterin levels decreased gradually over time in patients with COVID-19 (p < 0.001). Moreover, ROC analysis found that the best cut-off value for diagnosing COVID-19 patients based on plasma neopterin levels was 38.85 nmol/L with 70% sensitivity and 82% specificity (AUC, 0.74 [0.69–0.82], p < 0.05). We also found an increase in neopterin production with increasing age (p < 0.001).ConclusionOur findings contribute to our growing understanding of neopterin levels as a promising biomarker for the detection of COVID-19 cases in sub-Saharan Africa.

Assessment of Suitable Growth Condition of Salvinia spp. for Sustainability of Floating Agriculture to Mitigate Climate Change Challenges on Crop Cultivation

Floating agriculture is an indigenous cultivation practice in the southern waterlogged areas of Bangladesh. Water hyacinth and other macrophytes were utilized in this agricultural method to prepare the floating bed. Among the numerous macrophytes used as mulch in floating beds, Salvinia spp. is a common species. To meet the local farmer's demand, it is necessary to improve the growth rates of these macrophytes so that they can be available year-round for preparing floating beds. For this purpose, an experiment was conducted to assess the growth conditions of Salvinia spp. under various nutritional media. Salvinia molesta and Salvinia cucullata were cultivated in four healthy media to carry out the experiment. The experimental design used in the present investigation was a split-plot arrangement in a Randomized Block Design (RBD), where four nutrients (NO3-; PO43-; NH4+ and Urea) were assigned to the main plots, while five different concentrations of nutrient (0, 1, 5, 10 and 15 mM) were given to the sub-plots. Salvinia spp. growth was examined in terms of morphological and physiological responses and the distribution of nutrients. The highest relative growth rate (RGR) and biomass production (g DW) were found in the medium with one mM and five mM PO43- in the case of S. molesta and one mM NH4+ in the case of S. cucullata. It was also observed that the plant growth rate became suppressed when this macrophyte grew above the 5mM of nutrient concentration, and this suppressed condition turned into a toxic state when the organic nutrient concentration reached a concentration of 15 mM or above. In the case of Urea, this harmful condition starts with a concentration of 5 mM or more for S. molesta and any concentration for S. cucullata.Nonetheless, very few variations were found in how various plant tissues and species absorbed mineral concentrations. The overall findings from this experiment indicate that both PO43- and NH4+ concentrations of 1 mM and 5 mM are comparatively more suitable than NO3- and Urea for the growth and multiplication of Salvinia spp. However, more research is required in addition to varying the quantities of phosphate and ammonium to make a definitive choice. J. of Sci. and Tech. Res. 5(1): 53-62, 2023

The Empirical Examination of the Effect of Environmental, Social, and Governance (ESG) Performance on Enterprise Value: Focusing on Real Estate Agencies

In the 21st century, modern production techniques and technological advancements have yielded remarkable development achievements, yet they are accompanied by challenges posed by energy and resource scarcity, ecological and environmental strain, and pressing social issues. Recently, the spotlight has shifted to environmental, social, and governance (ESG) factors, as negative social events have heightened public awareness. The impact of corporate ESG performance on society is significant, and the public's perception can, in turn, affect corporate profitability and overall value. As such, investors are increasingly realizing the importance of considering ESG metrics in their decisions. While ESG investment is still in its nascent stage domestically, it has garnered widespread attention and is rapidly gaining momentum. Indices and related derivative products themed around ESG, such as the ESG Beauty 50 index, have been launched on domestic exchanges like the Shanghai Stock Exchange and China Securities Index. Taking the real estate industry as a case study, this paper aims to accomplish the following through a combination of literature research and empirical analysis: Firstly, to assess whether ESG performance varies across companies with diverse characteristics (such as state-ownership status, enterprise size, and so forth) and explore its differential impact on enterprise value. Secondly, to raise awareness among company managers regarding the critical role of ESG performance in shaping the future sustainability and long-term value of their organizations.

Improvement of the Estimation of the Vertical Crustal Motion Rate at GNSS Campaign Stations Based on the Information of GNSS Reference Stations

With the enrichment of GNSS data and the improvement in data processing accuracy, GNSS technology has been widely applied in fields such as crustal deformation. The Crustal Movement Observation Network of China (CMONOC) has provided decades of Global Navigation Satellite System (GNSS) data and related data products for crustal deformation research on the Chinese mainland. The coordinate time series of continuously observed reference stations contain abundant information on crustal movements. In contrast, the coordinate time series of periodically observed campaign stations have limited data, making it difficult to separate or remove instantaneous non-tectonic movements from the time series, as performed with reference stations, to obtain a stable and reliable crustal movement velocity field. To address this issue, this paper proposes a method to improve the estimation of crustal movement velocity at campaign stations using the information of neighboring reference stations. This method constructs a Delaunay triangulation of reference stations and fits the periodic movement of each campaign station using an inverse distance weighted interpolation algorithm based on the reference station information. The crustal movement velocity of the campaign stations is then estimated after removing the periodic movement. This method was verified by its application to the estimation of the vertical motion rate at some reference and campaign stations in Yunnan Province. The results show that the accuracy of vertical motion rate estimation for virtual and real campaign stations improved by an average of 24.4% and 9.6%, respectively, demonstrating the effectiveness of the improved method, which can be applied to estimate crustal movement velocity at campaign stations in other areas.

Mimicking Marine Conditions to Improve Prodigiosin Yields in Bioreactor

Prodigiosin is a red bacterial pigment with great potential as a natural dye and drug precursor, while presenting several pharmacological properties, including antimicrobial and anticancer activities. Its commercialization for biomedical applications, however, remains scarce. The major limitations are related to the lack of efficient bioprocesses and scaling up from laboratory to production. In the present work, the upstream process for prodigiosin production was developed using a marine Serratia rubidaea isolated from a sample collected near a shallow-water hydrothermal vent. The yield of product per biomass was found to be influenced by the cell concentration in the inoculum. The system was scaled up to 2 L stirred tank reactors with two different vessel geometries. It was shown that the vessel geometry and a cascade control mode for regulating the dissolved oxygen concentration influenced the volumetric oxygen mass transfer coefficient (kLa) and thus prodigiosin production. To improve product yields, strategies to mimic the aeration conditions found at the sampling site were tested. When the inoculum was grown for 5 h at 200 rpm and for 19 h at 25 rpm, which significantly decreased the oxygen available, the cells produced 588.2 mgproduct/gbiomass, corresponding to a production of 1066.2 mg of prodigiosin in 24 h and a productivity of 36.1 mgproduct/(L.h). This is a 3.7-fold increase in prodigiosin yield and a 4.5-fold increase in productivity in relation to when no particular strategy was promoted. Additionally, it was shown that lipid analysis and flow cytometry may be used as reliable at-line analytical tools, allowing the monitoring of cell condition and prodigiosin production during fermentation.

Epitopes screening and vaccine molecular design of PEDV S protein based on immunoinformatics

Porcine epidemic diarrhea virus (PEDV) is a serious disease that poses a significant threat to the pig industry. This study focused on analyzing the Spike protein of PEDV, which harbors crucial antigenic determinants, in identifying dominant epitopes. Immunoinformatics tools were used to screen for B-cell, CD4+ and CD8+ predominance epitopes. These epitopes were then connected to the N-terminal of ferritin to form a self-assembled nanoparticle vaccine. Various physical and chemical properties of the candidate vaccine were analyzed, including secondary structure prediction, tertiary structure modeling, molecular docking, immune response simulation and computer cloning. The results demonstrated that the candidate vaccine was antigenic, soluble, stable, non-allergic, and formed a stable complex with the target receptor TLR-3. Immune simulation analysis showed that the candidate vaccine effectively stimulated both cellular and humoral reactions, leading to increased related cytokines production. Furthermore, efficient and stable expression of the candidate vaccine was achieved through reverse translation in the Escherichia coli K12 expression system following codon optimization and in silico cloning. The developed nanoparticle candidate vaccine in this study holds promise as an effective PEDV vaccine candidate, offering a new approach for the research, development and improvement of vaccines targeting porcine enteric diarrhea coronavirus.

A Conterminous United States–Wide Validation of Relative Tidal Elevation Products

Recent large-scale spatial products have been developed to assess wetland position in the tidal frame, but nationwide comparisons and validations are missing for these products. Wetland position within the tidal frame is a commonly used characteristic to compare wetlands across biogeomorphic gradients and factors heavily into wetland vulnerability models. We utilize a dataset of 365 surface elevation table stations across the conterminous USA containing ground-surveyed tidal datum and elevation data to validate two gridded, conterminous USA–wide relative tidal elevation products. We identified substantial differences between our ground-surveyed dataset and the gridded products, with the Gulf coast exhibiting the greatest error (p < 0.0001, n = 140). Error in relative tidal elevation products varied by coast, tidal range, and latitude. These differences in errors indicate that gridded relative tidal elevation products may be more accurate in coastal wetlands with larger tidal ranges (> 30 cm) and are less accurate in freshwater wetlands near the coast. This paper makes advances in understanding why relative tidal elevation differences occur among national datasets and identifies areas of future work that could support more robust vulnerability models.

Review: Impact of food safety on global trade.

Food safety encompasses the supply and assurance of safe, high-quality food for consumers. It is a crucial aspect of food security, gaining greater global attention due to the increasing number of widespread foodborne incidents. International trade is expanding as countries increasingly rely on each other to secure a sufficient and diverse food supply. Beyond this, concerns about food safety have become more prevalent due to various factors. Therefore, this review aims to investigate the effects of food safety-associated risks on the international trade of food and related products. A total of 37 published studies retrieved using different search engines were included in this review. This review revealed that because of rapid population growth and rising food demand in developing nations, agricultural intensification is growing. It has been found that foodborne illnesses and associated discrepancies can impede the international trade of food commodities. Trade bans due to the fear of foodborne illnesses are growing. The consequences of foodborne diseases are multifaceted and include financial losses from trade restrictions, medical costs for prevention or control, resource depletion and a decline in food production. The overall effects are increased international trade tensions and livelihood vulnerability to poverty, notably for small-scale livestock producers. Potential food contaminants include microbes, pesticides, pharmaceutical residues, heavy metals and fraudulent such as improper food processing, mislabelling, poor packaging, adulteration and substitution. Hence, countries are encouraged to harmonize the rights and duties set by the World Trade Organization under sanitary and phytosanitarys to maximize their advantages in global markets. Based on this evidence, we recommend that each country develop and integrate regulations that would ensure the safety of both domestic and international food production systems. Furthermore, the global community should either revise the current functioning food regulatory and monitoring body or establish a more genuine collaborative network.

Perovskite versus Standard Photodetectors.

Perovskites have been largely implemented into optoelectronics as they provide several advantages such as long carrier diffusion length, high absorption coefficient, high carrier mobility, shallow defect levels and finally, high crystal quality. The brisk technological development of perovskite devices is connected to their relative simplicity, high-efficiency processing and low production cost. Significant improvement has been made in the detection performance and the photodetectors' design, especially operating in the visible (VIS) and near-infrared (NIR) regions. This paper attempts to determine the importance of those devices in the broad group of standard VIS and NIR detectors. The paper evaluates the most important parameters of perovskite detectors, including current responsivity (R), detectivity (D*) and response time (τ), compared to the standard photodiodes (PDs) available on the commercial market. The conclusions presented in this work are based on an analysis of the reported data in the vast pieces of literature. A large discrepancy is observed in the demonstrated R and D*, which may be due to two reasons: immature device technology and erroneous D* estimates. The published performance at room temperature is even higher than that reported for typical detectors. The utmost D* for perovskite detectors is three to four orders of magnitude higher than commercially available VIS PDs. Some papers report a D* close to the physical limit defined by signal fluctuations and background radiation. However, it is likely that this performance is overestimated. Finally, the paper concludes with an attempt to determine the progress of perovskite optoelectronic devices in the future.

An Exploratory Study on the Rapid Detection of Volatile Organic Compounds in Gardenia Fruit Using the Heracles NEO Ultra-Fast Gas Phase Electronic Nose.

Gardenia fruit is a popular functional food and raw material for natural pigments. It comes from a wide range of sources, and different products sharing the same name are very common. Volatile organic compounds (VOCs) are important factors that affect the flavor and quality of gardenia fruit. This study used the Heracles NEO ultra-fast gas phase electronic nose with advanced odor analysis performance and high sensitivity to analyze six batches of gardenia fruit from different sources. This study analyzed the VOCs to find a way to quickly identify gardenia fruit. The results show that this method can accurately distinguish the odor characteristics of various gardenia fruit samples. The VOCs in gardenia fruit are mainly organic acid esters, ketones, and aldehyde compounds. By combining principal component analysis (PCA) and discriminant factor analysis (DFA), this study found that the hexanal content varied the most in different gardenia fruit samples. The VOCs allowed for the fruit samples to be grouped into two main categories. One fruit sample was quite different from the fruits of other origins. The results provide theoretical support for feasibility of rapid identification and quality control of gardenia fruit and related products in the future.

Preparation of Double-Layer Composite Coffee Filtration Nonwovens.

The coffee industry is developing rapidly in the world, and the use of coffee filtration nonwovens (CFNs) is becoming more and more extensive; however, there is a lack of standards and research for its production and trade, and the quality of related products on the market is uneven at present. Here, eight double-layer composite coffee filtration nonwovens (D-LCCFNs) were prepared by using 5 g/m2 and 10 g/m2 polypropylene (PP) melt-blown nonwovens (MNs), 20 g/m2 PP spunbonded nonwovens and 20 g/m2 viscose/ES fiber chemically bonded nonwovens, and the physical properties, morphology and the filtration effect of coffee and purified water for the prepared samples were tested. It was found that the surface density of the microfiber layer (MNs) in the D-LCCFNs was negatively correlated with the coffee filtration rate; when the microfiber layer in the D-LCCFNs was in direct contact with the coffee, the liquid started to drip later, and the filtration rate of the coffee was slower; the filtration rate of the samples with the viscose/ES chemically bonded nonwovens was very fast. However, the samples without viscose/ES fibers basically did not filter pure water much, but they could filter out the coffee liquid normally, and the samples' hydrophilicity increased significantly after filtering coffee.

The Elusive Truth of Cannabinoids for Rheumatic Pain.

Medical cannabis (MC) has entered mainstream medicine by a unique route. Regulatory acceptance as a medical product in many jurisdictions has bypassed the traditional evidence-based pathway required for therapies. Easier access to MC, especially related to recreational legalization of cannabis, has led to widespread use by patients for symptom relief of a variety of medical conditions and often without medical oversight. Musculoskeletal pain remains the most common reason for MC use. This review examines real-world issues pertaining to MC and offers some guidance for clinical care of patients with rheumatic diseases being treated with MC. Controlled clinical studies of cannabis products in patients with rheumatic diseases have been small and tested a range of compounds, routes of administration, and clinical populations, limiting our ability to generate conclusions on MC's effectiveness in this population. Observational cohort studies and surveys suggest that use of MC and related products in patients with rheumatic diseases improves pain and associated symptoms but is commonly accompanied by mild to moderate side effects. Conflicting evidence contributes to practitioner and patient uncertainty regarding the use of MC for rheumatic disease-related pain. Despite promising preclinical and observational evidence that MC and cannabis-derived compounds are useful in the management of rheumatic disease-related pain, there remains limited high-quality clinical evidence to substantiate these findings. There are a significant number of clinical trials on this topic currently planned or underway, however, suggesting the next decade may yield more clarity. Nevertheless, given that many people with rheumatic diseases are using cannabis products, healthcare professionals must remain apprised of the evidence pertaining to cannabinoids, communicate such evidence to patients in a meaningful way that is free from personal bias and stigma, and maintain strong collaborative clinical care pertaining to MC.