Geographical discrimination of Asian red pepper powders using 1H NMR spectroscopy and deep learning-based convolution neural networks.

This study investigated an innovative approach to discriminate the geographical origins of Asian red pepper powders by analyzing one-dimensional 1H NMR spectra through a deep learning-based convolution neural network (CNN). 1H NMR spectra were collected from 300 samples originating from China, Korea, and Vietnam and used as input data. Principal component analysis - linear discriminant analysis and support vector machine models were employed for comparison. Bayesian optimization was used for hyperparameter optimization, and cross-validation was performed to prevent overfitting. As a result, all three models discriminated the origins of the test samples with over 95 % accuracy. Specifically, the CNN models achieved a 100 % accuracy rate. Gradient-weighted class activation mapping analysis verified that the CNN models recognized the origins of the samples based on variations in metabolite distributions. This research demonstrated the potential of deep learning-based classification of 1H NMR spectra as an accurate and reliable approach for determining the geographical origins of various foods.

No drug-drug interactions between selective prolyl-tRNA synthetase inhibitor, bersiporocin, and pirfenidone or nintedanib in healthy participants.

Bersiporocin, a potent and selective prolyl-tRNA synthetase inhibitor, is expected to show a synergistic effect with pirfenidone or nintedanib in patients with idiopathic pulmonary fibrosis. To validate the combination therapy of bersiporocin with pirfenidone or nintedanib, a randomized, open-label, two-part, one-sequence, three-period, three-treatment study was designed to evaluate the effect of drug-drug interactions (DDI) regarding their pharmacokinetics, safety, and tolerability in healthy participants. In addition, the pharmacokinetic profiles of the newly formulated, enteric-coated bersiporocin tablet were evaluated after single and multiple administrations. The potential effects of cytochrome P450 2D6 (CYP2D6) genotyping on bersiporocin pharmacokinetics and DDI were also explored. In Part 1, participants were sequentially administered a single dose of pirfenidone 600 mg, a single dose of bersiporocin 150 mg followed by multiple doses, and bersiporocin in combination with pirfenidone. In Part 2, participants were sequentially administered a single dose of nintedanib 150 mg, multiple doses of bersiporocin 150 mg, and bersiporocin in combination with nintedanib. Forty-six participants completed the study. There was no significant pharmacokinetic DDI between bersiporocin, and pirfenidone or nintedanib. All adverse events (AEs) were mild to moderate and did not include serious AEs, suggesting bersiporocin alone or in combination therapy were well-tolerated. The newly formulated bersiporocin 150 mg tablet showed a moderate accumulation index. There was no significant difference in the pharmacokinetic profiles after administration of bersiporocin alone or in combination therapy between CYP2D6 phenotypes. In conclusion, there are no significant DDI regarding the pharmacokinetics, safety, and tolerability of bersiporocin administration with pirfenidone or nintedanib.

A Double-Blind, Randomized, Placebo-Controlled, Phase II Clinical Study To Evaluate the Efficacy and Safety of Camostat Mesylate (DWJ1248) in Adult Patients with Mild to Moderate COVID-19.

Although several antiviral agents have become available for coronavirus disease 2019 (COVID-19) treatment, oral drugs are still limited. Camostat mesylate, an orally bioavailable serine protease inhibitor, has been used to treat chronic pancreatitis in South Korea, and it has an in vitro inhibitory potential against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study was a double-blind, randomized, placebo-controlled, multicenter, phase 2 clinical trial in mild to moderate COVID-19 patients. We randomly assigned patients to receive either camostat mesylate (DWJ1248) or placebo orally for 14 days. The primary endpoint was time to clinical improvement of subject symptoms within 14 days, measured using a subjective 4-point Likert scale. Three hundred forty-two patients were randomized. The primary endpoint was nonsignificant, where the median times to clinical improvement were 7 and 8 days in the camostat mesylate group and the placebo group, respectively (hazard ratio [HR] = 1.09; 95% confidence interval [CI], 0.84 to 1.43; P = 0.50). A post hoc analysis showed that the difference was greatest at day 7, without reaching significance. In the high-risk group, the proportions of patients with clinical improvement up to 7 days were 45.8% (50/109) in the camostat group and 38.4% (40/104) in the placebo group (odds ratio [OR] = 1.33; 95% CI, 0.77 to 2.31; P = 0.31); the ordinal scale score at day 7 improved in 20.0% (18/90) of the camostat group and 13.3% (12/90) of the placebo group (OR = 1.68; 95% CI, 0.75 to 3.78; P = 0.21). Adverse events were similar in the two groups. Camostat mesylate was safe in the treatment of COVID-19. Although this study did not show clinical benefit in patients with mild to moderate COVID-19, further clinical studies for high-risk patients are needed. (This trial was registered with ClinicalTrials.gov under registration no. NCT04521296).

Optimization-based method removing highlights with flash/no-flash image.

This paper proposes an optimization-based method to remove highlights from specular surfaces without any special device other than a flash, or any limited capturing environment required by existing methods. Accordingly, we formulate an optimization problem to estimate a highlight-free image from the difference image between a flash/no-flash image pair. This optimization will automatically find and recover erroneous pixels (saturated pixels and remaining highlights) in the difference image. The optimization is designed to recover erroneous pixels using the intensities of their neighboring pixels while retaining the other correct pixels to preserve the original image details. We evaluated the proposed method qualitatively and quantitatively through experimentation using datasets of synthetic and real-world scenes. In the experiments, the proposed method outperforms existing state-of-the-art methods.

Evaluation of Utility of the Cement Solidification Process of Waste Ion Exchange Resin.

The present study aimed to evaluate the utility of the cement solidification process for stably disposing of waste ion exchange resin generated during the treatment of radioactive wastewater. The cement solidification process using the in-drum mixing system was selected to be used for the solidification process of waste ion exchange resins. The disposal safety of waste forms was evaluated according to the waste acceptance criteria (WAC) applicable to domestic waste disposal sites, and the tests were conducted for six test items provided in the WAC. A total of 15 representative samples were collected from the waste-form drums produced using the optimum operating conditions, and their structural stability for disposal considerations was evaluated. In addition, the leaching index of the samples was 11.05, 10.12, 8.39 for Co, Sr, and Cs, respectively, and it was found to exceed 6, the leaching index standard of WAC. The results confirmed that cement waste forms including waste ion exchange resins produced through this process were considered to be conforming to the requirements for disposal safety.

Rollback Ensemble With Multiple Local Minima in Fine-Tuning Deep Learning Networks.

Image retrieval is a challenging problem that requires learning generalized features enough to identify untrained classes, even with very few classwise training samples. In this article, to obtain generalized features further in learning retrieval data sets, we propose a novel fine-tuning method of pretrained deep networks. In the retrieval task, we discovered a phenomenon in which the loss reduction in fine-tuning deep networks is stagnated, even while weights are largely updated. To escape from the stagnated state, we propose a new fine-tuning strategy to roll back some of the weights to the pretrained values. The rollback scheme is observed to drive the learning path to a gentle basin that provides more generalized features than a sharp basin. In addition, we propose a multihead ensemble structure to create synergy among multiple local minima obtained by our rollback scheme. Experimental results show that the proposed learning method significantly improves generalization performance, achieving state-of-the-art performance on the Inshop and SOP data sets.

Simple, green organic acid-based hydrometallurgy for waste-to-energy storage devices: Recovery of NiMnCoC2O4 as an electrode material for pseudocapacitor from spent LiNiMnCoO2 batteries.

A simple, green approach to recover NiMnCoC2O4 as an electrode material for high-performance pseudocapacitors from spent LiNiMnCoO2 (NMC) batteries is proposed. Four strategic metals (Li, Ni, Co, and Mn) were leached from spent NMC batteries using several organic acids as model green leachants. Among the various candidates of green leaching agents, 2 M citric acid and 5 wt% glucose were selected as the leachant and reductant, respectively. Microwave irradiation was conducted during the leaching step to maximize the performance of the leaching rate and efficiency. The leaching efficiencies within 0.5 h for Ni(II), Li(I), Mn(II), and Co(II) were 90.7 ± 1.6%, 98.3 ± 2.4%, 94.9 ± 4.3%, and 95.6 ± 1.4%, respectively, and were thus as efficient as using aqua regia leaching. After the leaching process, divalent metal ions, that is, Ni(II), Co(II), and Mn(II), were immediately separated at room temperature using oxalic acid. The recovered samples were not further treated and used directly for energy storage applications. The recovered NiMnCoC2O4⋅nH2O has been demonstrated as a promising electrode for pseudocapacitors, providing a specific capacitance of 1641 F/g, good rate-retention capability (80% of low-current capacitance), and good cycle stability over 4000 charge-discharge cycles.

Organic acid-based linear free energy relationship models for green leaching of strategic metals from spent lithium-ion batteries and improvement of leaching performance.

Water-soluble organic acids (e.g., acetic acid, acrylic acid, and ascorbic acid), as green leachants, have been applied to leach strategic metals (Ni, Li, Mn, and Co) from spent lithium-ion batteries (LIBs). Organic acid-based linear free energy relationship models were developed and simulated to predict the feasibility of the leaching efficiency for each of the strategic metals based on in silico calculated descriptors. The developed models, with accuracy (R2) of 0.747-0.831, reveal that hydrogen bond acidity of organic acids promotes the leaching efficiency, whereas molecular volume or excess molar refraction inhibits the efficiency. Furthermore, toxicity (lethal dose 50%) of organic acids was discussed along with the predicted leaching efficiency to explore more green and efficient organic acids. Considering both toxicity and leaching efficiency, citric acid was selected as a green and efficient leachant. To more improve the leaching performance (rate and efficiency) of citric acid, glucose as a green reductant and microwave treatment were additionally applied. Under the selected conditions, the leaching efficiencies after 1 h for Ni, Li, Mn, and Co were enhanced up to 98.3%, 99.1%, 98.7%, and 97.7%, respectively.

Polyethyleneimine impregnated alginate capsule as a high capacity sorbent for the recovery of monovalent and trivalent gold.

For the first time, a polyethyleneimine-impregnated alginate capsule (PEIIAC) with a high adsorption capacity is developed for the recovery of monovalent and trivalent gold from an acidic solution. The strategy results in a new type of adsorbent, polyethyleneimine impregnated alginate capsule (PEIIAC) with a core-shell structure having a large number of amine groups as cationic binding site, facilitating maximum uptake of anionic auric chloride. The maximum uptake of PEIIAC was 3078 and 929 mg/g for Au (III) and Au (I), respectively, are recordable compared to other reported adsorbents to date. The as-prepared material was executed to check the sorption efficacy for Au (III) and Au (I) in the pH range of 1-12. With an increment in pH, the uptake capacity for Au (III) increased, while the uptake capacity for Au (I) decreased. The FTIR, XRD, and XPS studies revealed that the gold adsorption mechanism includes ionic interactions and reduction, wherein the amine, hydroxyl, and carboxyl groups are involved. The capsule showed a higher adsorption efficiency than other reported sorbents, making the material applicable in acidic solutions for the recovery of Au (I) and Au (III).

Prediction of organic pollutant removal using Corynebacterium glutamicum fermentation waste.

The disposal of bio-waste (e.g., Corynebacterium glutamicum) produced by the fermentation industry is a serious problem and has a negative impact on economic returns. Some fermentation waste can be recycled as livestock feed, but much cannot be used. Therefore, other recycling methods must be developed to increase its applications, for example, as an environmentally friendly adsorbent for the removal or recovery of chemicals. To broaden its application as an adsorbent, we carried out comprehensive experimental and theoretical analysis. From the experiments, adsorption affinity values between C. glutamicum and micropollutants were measured, and, based on the experimental values, we developed a predictive model. The experimental results reveal that the degree of adsorption is dependent on the structural properties of the micropollutants. In particular, the adsorbent has remarkable adsorption ability toward cations, whereas anionic and neutral compounds interact weakly with the adsorbent. In addition, we found that adsorption is affected by the sodium chloride concentration. Briefly, an increase in salt concentration increases the adsorption of anions, whereas the opposite behavior is observed for cations. In contrast, the adsorption of neutral compounds was not affected by the presence of salt. The modeling studies revealed that a linear free energy relationship model can be used to predict the adsorption affinity. Based on the developed model, we found that hydrogen-bond basicity, anionic coulombic interactions, and molecular volume are the main contributing factors to the adsorption model. However, to achieve the best predictability (a coefficient of determination (R2) of 0.902), additional parameters, such as the dipolarity/polarizability and dispersive interaction, should be included. This indicates that adsorption is a product of complex interactions.

Self-coagulating polyelectrolyte complexes for target-tunable adsorption and separation of metal ions.

Metal-containing wastes in aquatic environments lead to public health hazards and valuable resource lose. Metal-bearing wastewater must be treated to remove heavy metals or recover precious metals. To achieve these, target-tunable adsorbents that bind cationic and anionic metal species were developed through facile polyelectrolyte complexation using polyethylenimine (PEI) and polyacrylic acid (PAA). Utilizing the properties of the two polyelectrolytes and pKa variabilities, stable tunable adsorbents were fabricated in water without additional solvents. The homogenous complex adsorbents were strategically synthesized via dissolution in 0.1 M NaOH and drop-wise addition of 1 M HCl, followed by crosslinking with glutaraldehyde. Consequently, the adsorbents in alternating weight ratios of 4:1 and 1:4 (PEI:PAA) exhibited good tunability and adsorption properties. The maximum single metal adsorption capacities were 1609.7 ± 49.6 and 558.6 ± 9.67 mg/g for gold and cadmium, respectively. The pseudo-second-order model fitted the kinetics data more appropriately and was recognized as the rate controlling step. In a binary mixture, gold selectivity was observed to be influenced by adsorption-reduction mechanism, which was elucidated by XRD and XPS. Moreover, the adsorbents demonstrated NO3- sequestration properties, a feat deemed important for environmental remediation of nitrate ions. Finally, sequential separation was achieved with ethylenediaminetetraacetic acid (EDTA) and acidified thiourea.

Sequential recovery of gold and copper from bioleached wastewater using ion exchange resins.

Numerous studies have sought to address the extraction of metals from printed circuit boards by employing bioleaching process. However, separation and recovery of the bioleached metals have always been a bottleneck. Herein, we demonstrate effective recovery of bioleached Au and Cu via selective separation using ion exchange resins. pH-edge experiments revealed high affinity of Amberjet™ 4200 resin towards Au (adsorption capacity > 98%) over the entire pH range from pH 2-10, whereas Amberlite IRC-86 resin recorded very high Cu adsorption at around pH 5. Therefore, a two-step sequential process was designed for the effective separation and recovery of Au and Cu. In the 1st step, Au was completely recovered by using the Amberjet™ 4200 at the natural pH of 7.5. Subsequently, the Au-free solution was adjusted to pH 5 and Cu was recovered by using Amberlite IRC-86 (2nd step). Consequently, 98.7% Au and 78.9% Cu were successfully recovered. Therefore, this study provides a technical guideline for the selective recovery of Au and Cu from bioleached wastewater, which promotes effective waste minimization and efficient resource recovery.

Recovery of gold via adsorption-incineration techniques using banana peel and its derivatives: Selectivity and mechanisms.

In this study, banana peel (BP) and its derivatives after sequential extraction of biochemical components were evaluated for selective recovery of gold. In-depth instrumental characterizations including XPS, FTIR, XRD and HR-TEM were performed to understand the adsorption mechanisms. The biomass after lipid extraction, BP-L, demonstrated very good affinity and selectivity towards gold. In multi-metal systems containing 100 mg/L of Pt(IV), Au(III), Pd(II), Zn(II), Co(II), Ni(II) and Li(I), the selectivity coefficient increased from 978.45 in BP to 2034.70 in BP-L. Moreover, the equilibrium gold uptake was improved and reached 475.48 ± 3.08 mg/g owing to reduction-coupled adsorption mechanisms. The BP-L also showed improved gold nanoparticle formation properties that were pH-dependent. In a strategic adsorption-combined incineration process, metallic gold reaching 99.96% in purity was obtained. The BP and its derivative, BP-L have thus shown potentials for multiple applications in the areas of precious metal recovery and nanoscience.

Simultaneous scavenging of persistent pharmaceuticals with different charges by activated carbon fiber from aqueous environments.

The adsorptive removal possibility of persistent pharmaceuticals with different charges by activated carbon fiber (ACF) was examined. The pharmaceuticals tested included carbamazepine (CBZ), propranolol (PRO), and diclofenac (DCF), in neutral, cationic, and anionic forms, respectively, which were frequently detected in sewage. The adsorption characteristics of ACF were identified according to the kinetics, isotherm, pH, and ionic strength experiments. The results revealed that ACF can effectively remove these pharmaceuticals, and the adsorption capacities of CBZ, PRO, and DCF by ACF were 1.27 ± 0.06, 1.07 ± 0.08, and 0.95 ± 0.08 mmol g-1, respectively. Moreover, the adsorption of ACF for CBZ was independent of pH and ionic strength, whereas that of anionic diclofenac decreased at alkaline pHs and high concentrations of NaCl. Using a syringe system packed with ACF mat, the scavenging ability of intermittently generated secondary sewage was evaluated. As a result, the residual concentration of PRO and CBZ could not be even detected after consecutive 10 runs in secondary sewage mixture solution. This indicates ACF has the powerful potential for removing pharmaceutical micropollutants in the actual aqueous solutions. FTIR and XPS analyses showed that hydrophobic and π-π interactions and hydrogen bonding contributed to the adsorption process.

Adsorptive removal of cationic tricyclic antidepressants using cation-exchange resin.

This study aimed to select a high-performance cation-exchange resin (CER) and estimate its uptake of positively ionized tricyclic antidepressants (TCAs), i.e., amitriptyline (AMI), imipramine (IMI), clomipramine (CLO), and desipramine (DES), which are frequently used, and detected in wastewater treatment systems. For the selection of the resin, the one-point check test of AMI in distilled water was examined using several CERs. As a result, the strong-acid polystyrene CER, Dowex 50WX4-200, was selected on the basis of its outstanding uptake of AMI. The maximum adsorption capacities of Dowex 50WX4-200 for removal of the TCAs ranged from 2.53 ± 0.20 mmol/g to 3.76 ± 0.12 mmol/g, which are significantly higher when compared with those of previously reported adsorbents. This is likely because the combination of electrostatic and π-π interactions between the TCAs and Dowex 50WX4-200 may lead to high uptakes of the TCAs. Additionally, the removal efficiency of DES as a representative of the TCAs was tested in actual wastewater system containing activated sludge and miscellaneous cations. Consequently, the removal efficiencies of the DES in distilled water, aerobic wastewater, and filtered wastewater were 95.68%, 77.99%, and 56.66%, respectively. It is interesting to note that the activated sludge could also contribute to adsorption of the DES, leading to increased removability, while the cations present in the wastewater acted as competing ions, decreasing the removal efficiency.

Evaluation of orange peel-derived activated carbons for treatment of dye-contaminated wastewater tailings.

Dyes are colored compounds which are visible even at trace concentrations. Due to their recalcitrance and esthetic persistence, certain methods are unable to effectively eliminate them. So far, adsorptive treatment using activated carbons (ACs) is one of the most successful methods. In this study, we have employed orange peel (OP) as a cost-effective alternative to the expensive coal- and coir-based precursors to synthesize ACs for cationic methylene blue (MB) and anionic methyl orange (MO) dye adsorption. The pre-carbonized OP was activated via H2SO4, NaOH, KOH, ZnCl2, and H3PO4 to study the effects of activation reagents on dye removal efficiencies and mechanisms. Among several isotherm models employed to fit the adsorption data, the Langmuir and Sips models sufficiently estimated the maximum equilibrium uptakes close to the experimental values of 1012.10 ± 29.13, 339.82 ± 6.98, and 382.15 ± 8.62 mg/g, for ZnCl2-AC (MO), ZnCl2-AC (MB), and KOH-AC (MB), respectively. The adsorption mechanisms were suggested to involve electrostatic binding, pi-pi interactions, hydrogen bonding, and electron donor-acceptor reactions. Consequently, more than 99% removal efficiency was achieved from a laboratory organic wastewater sample bearing ~ 35 mg/L of MB. The results thus suggest that the synthesized ACs from agricultural waste have the tendencies to be applied to real dye wastewater treatment.

Characterization of the residual biochemical components of sequentially extracted banana peel biomasses and their environmental remediation applications.

After consumption of the inner fleshy fruit, the banana peel like many other fruit peels is usually disposed of unprocessed. For sustainable development, agro-wastes including banana peels need to be converted into valuable products that will be beneficial to human and the environment. In this study, biochemical components including lipids, proteins and structural polysaccharides were sequentially extracted from banana peel, and the residuals were characterized by FE-SEM/EDX, FTIR, XRD, TGA/DSC, XPS and elemental analysis. Owing to rapid industrialization, toxic species such as metals and dyes are consistently released into the aquatic environments. Therefore, the residual biomass samples were evaluated for environmental remediation application. The adsorption performances were outstanding, with uptakes reaching 1034, 279 and 152 mg/g, for methylene blue, lead and platinum, respectively. This study thus suggests that sequential extraction and detailed characterization are useful for identification of key contributing components for development of high-performance agro-waste-based adsorbents for water treatment.

Clinical Relevance for Serum Cold-Inducible RNA-Binding Protein Level in Alopecia Areata.

BACKGROUND: Alopecia areata (AA), a chronic, relapsing hair-loss disorder, is considered to be a T-cell-mediated autoimmune disease. Cold-inducible RNA-binding protein (CIRP) belongs to a family of cold-shock proteins that respond to cold stress, and has been identified as a damage-associated molecular pattern (DAMP) molecule that triggers the inflammatory response. Recent studies have shown that high-mobility group box 1, another DAMP molecule, is elevated in serum and scalp tissue of AA patients, suggesting a relationship between DAMP molecules and the pathogenesis of AA. OBJECTIVE: To investigate the clinical significance of serum CIRP levels in AA. METHODS: The serum levels of CIRP were compared between 68 patients with AA and 20 healthy controls. Additionally, the correlation between CIRP level and various clinical parameters was evaluated. RESULTS: The serum CIRP levels were significantly higher in AA patients compared to healthy subjects. Moreover, there was an association between the serum CIRP level and clinical characteristics, such as disease duration and disease activity. However, there was no significant difference in the serum CIRP level among the clinical types of AA (AA multiplex, alopecia totalis, and alopecia universalis). CONCLUSION: These results suggest that CIRP may play a significant role in the pathogenesis of AA and could be a potential biologic marker for monitoring the disease activity of AA.

Experimental and QSAR studies on adsorptive interaction of anionic nonsteroidal anti-inflammatory drugs with activated charcoal.

Adsorptive interactions, namely adsorption capacity (qm) and affinity (b), between nonsteroidal anti-inflammatory drugs (NSAIDs) in anionic forms and commercial activated charcoal (AC), were estimated by isotherm experiment in a batch, and the properties were modeled based on the concept of quantitative structure-activity relationship (QSAR). Experimental results showed that AC had a high qm (0.38-0.67 mmol g-1) and b (14.03-930.8 L mmol-1) for the selected NSAIDs. In QSAR modeling, linear free energy relationship (LFER) descriptors of excess molar refraction (E), dipolarity/polarizability (S), and Coulombic interactions of anions (J-) were highly related to log qm, and the combination of the three terms could predict log qm in R2 of 0.97 and SE of 0.015 log unit. In the case of b, only single B term showed a good correlation with log b in R2 of 0.81. Additionally, the combination of hydrogen-bonding acceptors (HBAs) and molar volume (MV), which are easily calculable parameters, could also derive good predictability in R2 = 0.81 and SE = 0.26 log unit. Afterwards, validation of the QSAR models based on the leave-one-out cross-validation (Q2LOO) method showed that the models were acceptable.

Adsorptive interaction of cationic pharmaceuticals on activated charcoal: Experimental determination and QSAR modelling.

Due to high mobility and specific toxic actions of the ionizable pharmaceuticals in surface water with a normal range of pH, the pharmaceuticals should be removed before being discharged. Therefore, this study investigated the adsorptive interactions between cationic pharmaceuticals and a popular adsorbent (i.e., activated charcoal) frequently used in water treatment processes. For that, we performed isotherm experiments and then the results were plotted by Langmuir model to determine the adsorption affinity (b) and capacity (qm). Afterwards, to interpret the adsorption behaviors, two simple prediction models were developed based on quantitative structure-activity relationships (QSAR). In the modelling, molecular weight (MW), polar surface area (PSA), and octanol-water partitioning coefficient (log P) were used as model parameters. In the results, the combinations of these three parameters could predict the adsorption affinity and capacity in R2 of 0.85 and 0.80, respectively. The robustness of models was validated by leave-one-out cross-validation (Q2LOO) and the estimated Q2LOO values were 0.60 and 0.55 for the adsorption affinity and capacity, respectively, which are higher than the acceptability of standard i.e., 0.5.