Polymeric quaternary ammonium with three different alkyl chain lengths  were successfully introduced to obtain as q-PMMA-b-PDMAEMA. The q-PMMA-b-PDMAEMA in the nanofiber matrix was confirmed by C=O bands (1734 cm−1) in the Fourier transform infrared spectra. Nano-sized spherical protuberances were distributed on the surface as revealed by field emission scanning and transmission electron microscopies. The PVDF/GO/q-PMMA-b-PDMAEMA@PVA nanofibers has superhydrophilic properties (water contact angle = 0–20°) and the pure water flux was generally improved by increasing the alkyl chain length. When introducing the longest alkyl chain , the total fouling ratio was the lowest (49.99%) and the bacteria removal capacities after 60 min were the highest for both Escherichia coli (4.2 × 105 CFU/mg) and Staphylococcus aureus (6.1 × 105 CFU/mg) via growth inhibition and cytoplasmic membrane damage.Poly(vinylidene fluoride) (PVDF) is common polymer for electrospinning, however, its high hydrophobicity is a major drawback, which cause fouling. To introduce hydrophilicity and antibacterial activity, quaternary ammonium-functionalized amphiphilic diblock copolymers were synthesized and blended with a PVDF/graphene oxide (GO) solution, then, electrospun and coated with a hydrophilic polymer, poly (PVA). The amphiphilic block copolymer, consisting of a hydrophobic poly(methyl methacrylate) block and a hydrophilic poly[N,N-2-(dimethylamino)-ethyl methacrylate) block (PMMA- Poly(vinylidene fluoride) (PVDF) is one of the most common polymers for producing electrospun nanofibers used in microfiltration and ultrafiltration for industrial application owing to its high chemical resistance, good mechanical strength, thermal stability, and low cost3. However, the high hydrophobicity of PVDF is unsuitable for water treatment membranes, causing fouling, which results in reduced permeation fluxes and increased operating costs. To overcome this issue, many attempts have been made to improve the hydrophilicity of PVDF membranes, including hydrophilic polymer coating5, surface grafting6, blending amphiphilic polymers10, and introducing inorganic nanoparticles14. Surface grafting is one of simple and common chemical modification methods induced by plasma treatment, electron beam radiation, and O3/O2 pre-activation3, however, it could cause degradation of PVDF chains on the membrane surface due to introducing covalent bonding. On the other hand, coating a hydrophilic polymer layer on PVDF nanofibers is a simple and easy physical modification method to enhance hydrophilicity without changing chemical composition of PVDF; however, incompatibility has been observed between hydrophilic polymers and the hydrophobic PVDF matrix.Electrospun nanofibers have great potential to use in water treatment, such as large surface areas and high porosities, which are attributed to the 100–200 nm diameters of the fibers9. PVA is a promising hydrophilic polymer because of its high chemical resistance, thermal stability, low cost and non-toxicity to the environment16. Nevertheless, a hydrophilic surface alone is not sufficient to prevent fouling from biofilm formation, and antibacterial activity is also needed to overcome fouling.Amphiphilic polymers may be used as modifiers that have strong interactions with both the PVDF nanofiber matrix and hydrophilic polymers, such as poly (PVA)19. MMA was chosen for the hydrophobic part owing to its high compatibility with PVDF for homogeneous blending and high retention8. In addition, quaternary ammonium compounds are well-known to have broad-spectrum of antimicrobial activity against bacteria, algae, and fungi via long alkyl chains20. Polymeric quaternary ammonium groups with long alkyl chains were introduced to the PMMA-b-PDMAEMA to yield q-PMMA-b-PDMAEMA amphiphilic block copolymers. It was synthesized by atom transfer radical polymerization (ATRP) for PMMA-b-PDMAEMA in combination with a Menshutkin reaction to introduce cationic quaternary ammonium groups19. Rationally designed amphiphilic copolymers with the aforementioned beneficial features could be desirable candidates in the engineering field for modification of PVDF membranes, enabling tunable hydrophilicity and favorable interfacial control25.Amphiphilic block copolymers, consisting of a hydrophobic block of poly(methyl methacrylate) (PMMA) and a hydrophilic block of poly[N,N-2-(dimethylamino) ethyl methacrylate) (PDMAEMA), have been investigated to enhance both the hydrophilic polymer compatibility and antibacterial activity of PVDF nanofiber9. Sun et al.9 found that a PVDF/PMMA-b-PDMAEMA membrane slightly reduced protein adsorption and improved hydrophilicity compared with the pristine PVDF membrane. Kakihana et al.8 blended P(MMA-co-DMAEMA) containing quaternary ammonium groups in a PVDF membrane and showed a slight increase in antibacterial activity against Escherichia coli with one alkyl chain, however, the modified membrane exhibited a more hydrophobic nature than the pristine PVDF membrane. Therefore, two previous studies provided limited results of improving both hydrophilicity and antibacterial activity of PVDF membrane. To the best of our knowledge, blends of PVDF nanofibers with amine quaternized PMMA-b-PDMAEMA diblock copolymers (q-PMMA-b-PDMAEMA) with alkyl chains have not been investigated for enhancing anti-fouling activity through increased hydrophilicity owing to PVA coating layers and increased antibacterial activity by quaternary ammonium compounds (QACs). In our previous study26, graphene oxide (GO)/PVDF nanofibers showed better tensile strength, thermal stability, and antibacterial property than those of PVDF nanofibers, however, hydrophilicity is slightly enhanced. Generally, GO into PVDF membrane has been mainly studied to increase pollutant adsorption capacity28, improve antibacterial, and mechanical strength29, moreover, it can prevent the release of QACs from PVDF membranes, and achieve better QAC stability in membranes without sacrificing antibacterial activity30.A few studies on the synthesis and blending of amphiphilic block copolymers with MMA and DMAEMA have been performed to improve the hydrophilicity and antibacterial activity of PVDF membranes using the phase-inversion methodq-PMMA-b-PDMAEMA using electrospinning technology (PVDF/GO/q-PMMA-b-PDMAEMA nanofibers) to achieve high anti-fouling activity induced by superhydrophilicity and anti-bacterial properties. Specifically, 1) the synthesis of q-PMMA-b-PDMAEMA with different alkyl chain lengths using CRP-ATRP; 2) the preparation and characterization of PVDF/GO/q-PMMA-b-PDMAEMA nanofibers with PVA coating layer for microfiltration; and 3) the evaluation of anti-fouling activity using fouling reversibility test and anti-bacterial experiments  were investigated.Therefore, the aim of this study was to synthesize PVDF/GO nanofibers containing b-PDMAEMA diblock copolymers and the Menshutkin reaction for functionalization of the PDMAEMA block using 1-bromo alkyl chains of varying length of alkyl chains (from ethane to octane)  Fig. . After tght Fig. . In addi.21 Fig. .Figure 11H NMR spectroscopy  and 4.03 (d) ppm, which corresponding to PMMA and PDMAEMA, respectively  and six protons of dimethyl (f) groups, respectively  reaction was performed. Furthermore, the presence of alkyl chains onto the dimethylamino groups resulted in new broad peaks in the range of 1.13–1.54 ppm, which are denoted as h, h’, and h”  obtained for PVDF/GO/OBC@PVA nanofibers, which had the lowest water contact angle of 11.6° /PVDF membrane, which still showed a high water contact angle of 132° (PVDF membrane = 110°). Sun et al.9 showed that water contact of PVDF/PMMA-b-PDMAEMA membrane was still high as 76° compared with that of pristine PVDF membrane (98°). However, the water contact angles of the PVDF/GO@PVA, PVDF/GO/EBC@PVA, PVDF/GO/BBC@PVA, and PVDF/GO/OBC@PVA nanofibers were less than 20° at 1 s and finally 0° within 9 s via PVA coating and crosslinking step, indicating that the nanofibers became superhydrophilic. By methanol treatment, the PVA based nanofibers were stable in aqueous solution because residual water within the nanofibers were removed, which enhancing intermolecular polymer hydrogen bonding instead of PVA-water hydrogen bonding31. Similarly, Lu et al.5 found that a PVDF membrane coated with a PVA layer had a superhydrophilic nature. The incorporation of GO and q-PMMA-b-PDMAEMA can enhance PVA attachment on PVDF nanofibers compared with that on pristine PVDF nanofibers, which still had a water contact angle of 62° even coating with crosslinked PVA. The amounts of PVA coated on PVDF/GO, PVDF/GO/EBC, PVDF/GO/BBC, and PVDF/GO/OBC nanofibers were 0.043 ± 0.000 g/g, 0.063 ± 0.000 g/g, 0.062 ± 0.001 g/g, and 0.109 ± 0.000 g/g, respectively. Therefore, q-PMMA-b-PDMAEMA with longer alkyl chains tended to lower the water contact angle by increasing the attachment of the PVA layer.The average diameters of each of the nanofibers was in the range of 141.2–181.6 nm, as summarized in Table .6° Fig. . Before q-PMMA-b-PDMAEMA@PVA nanofibers can be predicted to be more positive owing to the effect of cationic charged groups (N+) in q-PMMA-b-PDMAEMA. However, the zeta potentials of the PVDF/GO/q-PMMA-b-PDMAEMA@PVA nanofibers were negative (−1.04 to −1.56 mV) because of the relatively high negative charge of the PVA (−4.7 mV) coating layer, which was bound more by q-PMMA-b-PDMAEMA, especially OBC.As the zeta potential values of PVDF/GO and PVDF/GO@PVA nanofibers are 1.26 and 0.31 mV, respectively, those of PVDF/GO/β-phase are the highest dipolar contributor among other phases32. The β-phase was dominantly observed with the strong peaks at 840 and 1276 cm−1 of all PVDF/GO/q-PMMA-b-PDMAEMA nanofibers  values of pristine PVDF, PVDF/GO, PVDF/GO/EBC, PVDF/GO/BBC and PVDF/GO/OBC nanofibers were 83.6, 87.7, 87.0, 87.5, and 87.2%, respectively. This indicating that β-phase is predominant of all PVDF/GO/q-PMMA-b-PDMAEMA nanofibers attributed by electrospinning process  and addition of GO (1.5 wt%); however, q-PMMA-b-PDMAEMA (0.5 wt%) showed a little impact on phase conformation of PVDF. Similarly, Sharma et al.33 insisted that PVDF electrospun nanofibers predominantly consisted of β-phase of PVDF because high-voltage conditions caused extensional forces on the PVDF chains to aligns the dipoles in one direction; also, incorporation of carbon nanotubes induce β-phase of PVDF due to charge accumulation. Abbasipour et al.34 reported that α-phase decreased dramatically by transforming the PVDF powder to the nanofiber and addition of GO in PVDF nanofibers enhanced the β-phase of PVDF via enhancing dipole-dipole forces. On the other hand, Cui et al.35 suggested that PMMA grafted TiO2 inhibited the orientation of PVDF chains during the co-electrospinning process leading decrease of β-phase content. A peak at 1734 cm−1 was only observed for in PVDF/GO/EBC, PVDF/GO/BBC and PVDF/GO/OBC nanofibers, not for PVDF/GO nanofibers , which corresponded to the trend observed for the zeta potentials. A negatively charged nanofiber surface can effectively inhibit protein adsorption. The anti-fouling properties were mainly enhanced by increasing the alkyl chain length among PVDF/GO/q-PMMA-b-PDMAEMA@PVA nanofibers with minor effects attributed to surface charge and flow pore diameters. All the nanofibers had similar mean flow pore diameters (0.23–0.45 μm), however, the PVDF/GO@PVA nanofibers had the largest pore diameter (0.96 μm); thus, BSA blockage was decreased relative to the other nanofibers and this could be easily backwashed. After backwashing with deionized water, the normalized flux of the PVDF/GO/OBC@PVA nanofibers was recovered to over 0.7, whereas those of the PVDF/GO/EBC@PVA and PVDF/GO/BBC@PVA nanofibers were 0.45 and 0.25, respectively. For PVDF/GO/OBC@PVA nanofibers, the increased hydrophilicity and slightly negatively charged surface owing to the long alkyl chain of OBC and the PVA layer successfully prevent BSA adsorption and enhance BSA desorption.In addition, 45.5% of the total fouling was reversible fouling for the PVDF/GO/OBC@PVA nanofibers. The highest BSA adsorption amount was calculated as 13.18 mg/cmq-PMMA-b-PDMAEMA@PVA nanofibers are summarized in Table E. coli and PVDF/GO/OBC@PVA nanofibers using E. coli and S. aureus. All bacteria grew well under the PVDF/GO@PVA nanofibers, whereas no growth of E. coli and S. aureus was observed when in direct contact with the PVDF/GO/OBC@PVA nanofibers. Antibacterial effects are enhanced by increasing the length of alkyl moieties, which strongly react with the cytoplasmic membranes of cells38. This finding demonstrated that q-PMMA-b-PDMAEMA could not diffuse to and be released into the agar owing to strong incorporation into the nanofibers, which maintained durable antibacterial activity. Thus, PVDF/GO/OBC@PVA nanofibers effectively inhibit the growth of bacteria.The results of the agar diffusion method using PVDF/GO/E. coli) and 7b (S. aureus), indicating that the incorporation of q-PMMA-b-PDMAEMA greatly enhanced the E. coli and S. aureus removal capacities compared with the minimum effect of PVDF/GO@PVA nanofibers. The removal capacity is assumed to be enhanced by two mechanisms: 1) positively charged ammonium ions on q-PMMA-b-PDMAEMA could attract negatively charged bacteria to the surface of the nanofibers and 2) cell damage by alkyl chains on q-PMMA-b-PDMAEMA could occur. The removal capacities increased as the length of the alkyl moieties increased, similar to the results of the agar diffusion method. Of the examined samples, the PVDF/GO/OBC@PVA nanofibers were the most effective for the removal of both E. coli (4.2 × 105 CFU/mg) and S. aureus (6.1 × 105 CFU/mg) after 60 min, and the removal capacities increased with increasing contact time. Similarly, Lin et al.39 found that the antibacterial properties against E. coli and S. aureus were increased by increasing the length of alkyl chains coated on cotton fabric from C12 to C18. Rawlinson et al.40 investigated the minimum inhibition concentration (MIC) of PDMAEMA using 13 bacteria species and found that it had antimicrobial effects on both gram-negative (MIC = 0.1–1 mg/mL) and gram-positive bacteria (MIC = 0.1–18 mg/mL).The results of the dynamic contact tests, as shown in Fig. E. coli and S. aureus), which were stained red, were clearly observed on the PVDF/GO/OBC@PVA nanofibers /PVDF membrane because DMAEMA could penetrate the outer membrane and disrupt the cytoplasmic membrane. Park et al.41 showed CLSM images to verify that benzyl triethylammonium chloride (a type of QAC)/PVA nanofibers could damage the cytoplasmic membranes of K. pneumoniae, E. coli and S. aureus. Chen et al.42 visualized the bacteria on a dodecyl dimethyl benzyl ammonium chloride (a type of QAC)/PVDF membrane using CLSM, showing that the membrane killed bacteria already attached to the surface, which led to a reduction of biofilm. Thus, PVDF/GO/OBC@PVA nanofibers can improve the anti-fouling activity, as contact with these nanofibers can inhibit bacterial growth and cause cytoplasmic cell membrane damage.LIVE/DEAD bacterial viability assays were conducted and observed using CLSM to verify the mechanism of bacteria removal by PVDF/GO/OBC@PVA nanofibers compared with that of PVDF/GO@PVA nanofibers Fig. . In thesers Fig. , howeverers Fig. . This reTwo modifications of PVDF nanofibers, blending with quaternary ammonium-functionalized amphiphilic block copolymers and PVA coating, were explored to overcome fouling via introducing superhydrophilic surfaces and antibacterial activity. These modifications effectively increased the hydrophilicity and pure water flux, while maintaining high porosity. The synthesized PVDF/GO/OBC@PVA nanofibers displayed a low total fouling ratio and inhibited protein adsorption, and the initial flux was recovered well after backwashing. In addition, the PVDF/GO/OBC@PVA nanofibers showed antibacterial activity against both gram-positive and gram-negative bacteria, as contact inhibited bacterial growth and caused cytoplasmic cell membrane damage. Therefore, the results highlight the potential use of the developed anti-fouling nanofibers for application in water purification.In this work, two modifications of PVDF nanofibers, blending with quaternary ammonium-functionalized amphiphilic block copolymers and PVA coating, were explored to overcome fouling via introducing superhydrophilic surfaces and antibacterial activity. These modifications effectively increased the hydrophilicity and pure water flux, while maintaining high porosity. The synthesized PVDF/GO/OBC@PVA nanofibers displayed a low total fouling ratio and inhibited protein adsorption, and the initial flux was recovered well after backwashing. In addition, the PVDF/GO/OBC@PVA nanofibers showed antibacterial activity against both gram-positive and gram-negative bacteria, as contact inhibited bacterial growth and caused cytoplasmic cell membrane damage. Therefore, the results highlight the potential use of the developed anti-fouling nanofibers for application in water purification.1H NMR spectrum of the reaction solution using signals corresponding to the polymer and the unreacted monomer. The reaction was terminated by cooling to −78 °C using liquid nitrogen. The quenched solution was then diluted with tetrahydrofuran (THF) (10 mL) and then passed through an aluminum oxide column. The solution was collected, and solvents were removed under reduced pressure. The concentrated solution was dropped into methanol. The obtained white precipitator was collected by filtration and then dried at 25 °C for 48 h under vacuum. Sequential copolymerization with PDMAEMA block was carried out using the PMMA as a macroinitiator. The synthesis procedure for the PDMAEMA second block was the same as the process used to synthesizes PMMA. All reagents, CuCl , anisole (8.3 mL), dNbpy , PMMA , and DMAEMA , were sequentially added to a baked 50 mL flask under Ar. The reaction was performed by stirring at 80 °C for 12 h. The resultants polymers exhibited 93% conversion. The reaction solution was quenched by immersing in liquid nitrogen. The purification procedure was the same as the method used for PMMA. The solvent for precipitation was only changed to hexane. 1H NMR  (ppm): 4.03 , 3.56 , 2.54 , 2.26 2), 1.2–2.1 , 0.7–1.1 .All reagents were purchased from Alfa Aesar and Sigma Aldrich. According to the typical ATRP technique, all reagents, CuCl , anisole (10.5 mL), 4,4′-di(5-nonyl)-2,2′-bipyridyl (dNbpy) , MMA , and a solution of ethyl 2-bromopropionate , were sequentially added to a baked reaction flask under Ar. The polymerization was allowed to proceed by heating at 80 °C for 14 h. The reaction conversion (97%) was determined from the b-PDMAEMA diblock copolymers were functionalized using the Menshutkin reaction to introduce with 1-bromo linear alkyl chains into the PDMAEMA block18. For this, PDMAEMA-b-PMMA  was dissolved in THF (7.7 mL). Thereafter, 1-bromoethane , 1-bromobutane , or 1-bromooctane  was added each to the above solution. The reaction was allowed to proceed by stirring at 60 °C for 24 h, yielding ca. 99% conversion and over 30 alkyl groups per chain. Purification was performed by precipitation in 300 mL of hexane after dilution with THF (10 mL). The three acquired q-PMMA-b-PDMAEMA resultants, ethyl-PMMA-b-PDMAEMA (EBC), butyl-PMMA-b-PDMAEMA (BBC), and octyl-PMMA-b-PDMAEMA (OBC), were dried at 25 °C for 48 h under reduced pressure. 1H NMR  (mg/L): 3.50–4.30 , 3.56 , 2.50–3.60 , 2.50–3.60 2), 1.2–2.1 , 1.13–1.54 x, x = 1, 3, 7), 0.7–1.1 . The number-average molecular weight (Mn) and molecular weight distribution (Mw/Mn) of each samples were measured using a JASCO PU-2080 plus SEC system equipped with RI-2031 and UV-2075 (254 nm detection wavelength) detectors using THF as the eluent at 40 °C with a flow rate of 1 mL/min. The samples were separated using four columns: Shodex-GPC KF-802, KF-803, KF- 804, and KF-805. 1H NMR spectra were recorded in CDCl3 at 25 °C on a 300 MHz Varian Unity INOVA spectrometer.Based on previously reported protocols, the obtained PMMA-q-PMMA-b-PDMAEMA nanofibers were fabricated by an electrospinning system  at room temperature and relative humidity of 25–30%. Based on prior experiments to examine the effect of various applied voltage (10–30 kV) and flow rate (0.1–1.5 ml/h), the optimum electrospinning conditions of PVDF/GO/q-PMMA-b-PDMAEMA solutions were determined as 0.2 mL/h of flow rate, 25 kV of applied voltage, and 15 cm of tip-to-collector distance. The PVDF/GO/q-PMMA-b-PDMAEMA blend solution was prepared by mixing a PVDF  solution (16 wt%) in N,N-dimethylformamide and acetone (v:v = 6:4) with 1.5 wt% of GO and 0.5 wt% of q-PMMA-b-PDMAEMA  at 50 °C. Each as-prepared solution was placed in a syringe with a metal needle (inner diameter of 0.5 mm). The PVDF/GO/q-PMMA-b-PDMAEMA nanofibers were then deposited on aluminum foil fixed to a rotating cylinder . Polymorphism and morphology of nanofiber depend on electrospinning conditions including mainly flow rate, applied voltage, and distance from the needle to the collector43, thus, optimized electrospinning condition was determined by prior experiments. The optimum electrospinning conditions for the PVDF/GO/q-PMMA-b-PDMAEMA solutions were investigated in the range of applied voltage (10–30 kV) and flow rate (0.1–1.5 ml/h) with tip-to-collector distance at 15 cm. There is no difference among three kinds of q-PMMA-b-PDMAEMA (0.5 wt%), while GO (1.5 wt%) play an important role in electrospinning PVDF/GO/q-PMMA-b-PDMAEMA solutions. Therefore, optimum electrospinning condition was determined equally as 0.2 mL/h of flow rate and 25 kV of applied voltage at 15 cm of tip-to-collector distance as no beaded nanofibers appeared and electrospinning stability and uniformity of the nanofiber was high. Pristine PVDF/GO nanofibers were also prepared by the same procedure without any q-PMMA-b-PDMAEMA additives for comparison. The as-prepared nanofibers were dipped into 2 wt% PVA solution in deionized water for 1 min, then soaked in methanol for 24 h to crosslink PVA, and then dried at 60 °C to give PVDF/GO/q-PMMA-b-PDMAEMA@PVA nanofibers.Electrospun PVDF/GO/q-PMMA-b-PDMAEMA@PVA nanofibers were evaluated using analytical techniques. Field emission scanning electron microscopy  and transmission electron microscopy  were examined for the morphology of the nanofibers. The average nanofiber diameter was determined by measuring nanofibers (n = 30) using ImageJ 1.43 u software  in each FESEM image. The thickness of each nanofiber was measured using a dial thickness gauge . To investigate the surface hydrophilicity, time-dependent water contact angles were measured by the sessile drop method using a contact angle analyzer  for 10 s. The amounts of PVA coated on the nanofibers were calculated by measuring weights before and after PVA coating. Fourier transform infrared (FT-IR) spectra of the nanofibers were recorded on a Nicolet iS10 spectrometer . The fractions of β-phase (F(β)) in the nanofibers were calculated from FT-IR spectra following the Lambert-Beer law, which is the equation  and Kα (6.1 × 104 cm2/mol) are the adsorption coefficients at the respective wavenumber.The characteristics of the PVDF/GO/ε) was calculated by a gravimetric method using the equation  = 0.93 bar) at room temperature. Deionized water (1.0 L) was used in the permeation flux tests and the permeation flux (Jw1) was estimated based on the equation , and t is the total permeation time (min).The flow pore diameters of the nanofibers were evaluated by a capillary flow porometer . The porosity (equation :2\documeJp) was measured. 3) Each nanofibers was backwashed with 1 L of deionized water and rinsed for 20 min at 170 rpm using a shaker . 4) Pure water was reinjected, and then the pure water flux (Jw2) was measured to determine the fouling reversibility. The flux recovery ratio (FRR) was calculated using equation  was chosen as a model protein to cause fouling of the nanofibers. BSA filtration tests were conducted using the following procedure (TMP = 0.93 bar): 1) Deionized water was filtered through each nanofibers  until the flux remained stable prior to BSA solution injection. 2) The BSA solution  was applied to each nanofibers and the flux of the BSA solution (t), reversible (Rr), and irreversible (Rirr) fouling ratios were calculated using equations  and Gram-positive Staphylococcus aureus (ATCC 6538), and three methods . First, the circular nanofibers (diameter = 1 cm) were put on TSA agar plates containing each bacterial inoculum. Then, the plates were incubated at 37 °C for 18 h and the zone of inhibition was observed. For dynamic contact tests, the nanofibers  were added to 30 mL of each bacterial solution . The mixtures were shaken at 150 rpm and 25 °C using a shaker  for the desired contact time . A plate counting method was used for enumeration of bacteria in suspensions after incubation at 37 °C for 24 h. The removal capacity of bacteria per unit mass (CFU/mg) of the nanofibers was calculated as follows:C0 is the initial bacteria concentration in solution (CFU/mL), Cs is the suspended bacteria concentration in the solution after reaction (CFU/mL), Ca is the detached bacteria concentration from the nanofibers after vigorously glass bead vortexing for 1 min in an autoclaved phosphate buffer solution (PBS) after the contact time (CFU/mL), and M is the mass of the nanofibers (mg). Finally, live and dead bacterial cells on the nanofibers were stained by LIVE/DEAD BacLight bacterial viability kit  and clearly observed by confocal laser scanning microscopy  after bacterial solution injection (50 µL) on each nanofibers (1 cm2) for 4 h at 37 °C.Antibacterial tests were performed with PVDF/GO@PVA, PVDF/GO/EBC@PVA, PVDF/GO/BBC@PVA and PVDF/GO/OBC@PVA nanofibers using two types of bacteria, Gram-negative Supplementary information


Their structures were proven by means of MALDI−TOF, IR and NMR studies. Effects of temperature, reaction time and carnitine dosage on the polymerization process were examined. Low-molecular weight poly(ε-caprolactone), polylactides and copolymers of ε−caprolactone and lactides were obtained by the polymerization of cyclic esters in the presence of a carnitine/SnOct Pharmacy and medicine are among the most important application fields of polymers. Polymers are used in production of prosthetic and dental materials, artificial organs, sutures and disposable hygiene products, active macromolecular pharmaceutical substances, blood substitutes, auxiliary materials and excipients, macromolecular prodrugs, polymeric drug delivery systems, therapeutic systems, etc. The polymeric prodrugs, drug delivery systems and therapeutic systems exhibit unique pharmacokinetics, body distribution and pharmacological efficacy ,14,15,16d,l-, l,l-lactide, ε−caprolactone; abbreviations: LA, LLA, CL, respectively). PLA, PLLA and PCL have been successfully synthesized by ring opening polymerization in the presence of cationic or anionic initiators, as well as coordinating and enzymatic catalysts  benzoic acid (HABA) as a matrix. Molecular mass values and molecular mass distributions of polymers were determined at 308 K on a Lab Alliance gel permeation chromatograph equipped with Jordi Gel DVB Mixed Bed (250 mm x 10 mm) columns and a refractive detector, using THF or chloroform as eluent (1 mL/min). The molecular mass scale was calibrated with polystyrene standards.The polymerization products were characterized by means of N,N-dimethylformamide (at 30ºC) using an Ubbelohde viscometer. Polymer molecular mass values were calculated from the Mark-Houwink formula using the following equation constants: K= 2.21·10−4 ml/g and α = 0.77 (for PLA), K= 3.25·10−4 ml/g and α = 0.77 (for PLLA), K = 1.94·10−4 and α = 0.73 (for PCL) [Polymer viscosity was measured in chloroform (at 25ºC) and for PCL) .Sample Availability: Contact the authors.


Whole-genome sequencing (WGS) will soon replace traditional phenotypic methods for routine testing of foodborne antimicrobial resistance (AMR). WGS is expected to improve AMR surveillance by providing a greater understanding of the transmission of resistant bacteria and AMR genes throughout the food chain, and therefore support risk assessment activities. At this stage, it is unclear how WGS data can be integrated into quantitative microbial risk assessment (QMRA) models and whether their integration will impact final risk estimates or the assessment of risk mitigation measures. This review explores opportunities and challenges of integrating WGS data into QMRA models that follow the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR. We describe how WGS offers an opportunity to enhance the next-generation of foodborne AMR QMRA modeling. Instead of considering all hazard strains as equally likely to cause disease, WGS data can improve hazard identification by focusing on those strains of highest public health relevance. WGS results can be used to stratify hazards into strains with similar genetic profiles that are expected to behave similarly, e.g., in terms of growth, survival, virulence or response to antimicrobial treatment. The QMRA input distributions can be tailored to each strain accordingly, making it possible to capture the variability in the strains of interest while decreasing the uncertainty in the model. WGS also allows for a more meaningful approach to explore genetic similarity among bacterial populations found at successive stages of the food chain, improving the estimation of the probability and magnitude of exposure to AMR hazards at point of consumption. WGS therefore has the potential to substantially improve the utility of foodborne AMR QMRA models. However, some degree of uncertainty remains in relation to the thresholds of genetic similarity to be used, as well as the degree of correlation between genotypic and phenotypic profiles. The latter could be improved using a functional approach based on prediction of microbial behavior from a combination of ‘omics’ techniques . We strongly recommend that methodologies to incorporate WGS data in risk assessment be included in any future revision of the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR. Antimicrobial resistance (AMR) represents a major threat to public health, with an estimated 700,000 deaths attributable to AMR every year in the world, and a projected 10 million deaths per year by 2050 in the absence of additional control measures . AMR is Salmonella carrying plasmid borne extended-spectrum beta-lactamase (ESBL) resistance  and person-to-person contact . The presistance . The relsistance . Howeverlobacter . AMR haslobacter . Until rlobacter .Salmonella and Campylobacter, in addition to some sequencing of resistant strains of Escherichia coli and Enterococcus collected from food-producing animals, retail meats and humans  and E. coli (since 2018) collected from agri-food samples and human clinical isolates  by public health and food safety laboratories has ramped up in recent years, and these new methods are set to replace traditional phenotypic methods for routine surveillance of AMR and other food safety hazards in the near future . In typid humans . Similarisolates .in silico speciation and sub-species level differentiation of isolates , as well as a description of the molecular mechanisms underlying observed resistance phenotypes, the use of WGS is expected to assist AMR surveillance by providing a greater understanding of the transmission of AMR bacteria and genes throughout the food chain, and therefore support risk assessment of foodborne AMR . This is typically done using homology-based algorithms such as BLAST  . Some prIn general, AMR genes can be located either on bacterial chromosomes or on MGEs, allowing AMR gene transmission via clonal spread or horizontal transfer, respectively. MGEs include factors which enable AMR genes to move within or between DNA molecules within the same cell , as well as factors which enable AMR genes to be transferred between bacterial cells  . PlasmidE. coli from humans, chicken meat, pigs and poultry farms, E. coli strains from poultry to humans, as had been suggested based on traditional, low-resolution typing methods , also known as Whole Genome Association Studies (WGAS), make it possible to identify genetic markers or genetic risk factors associated with increased pathogenicity or virulence. Briefly, GWAS identify molecular markers such as genes, k-mers, insertions/deletions or single base-pair changes  in the DNA sequence of an organism that are significantly associated with a phenotypic trait of interest  or virulence  of a given hazard. Prediction tools can be used to detect the presence of known pathogenicity or virulence genes, such as those determining the capacity of attachment, adhesion, invasion or replication of an isolate. As an example, the Center for Genomic Epidemiology (CGE) has developed web-based tools to identify acquired virulence genes among several bacterial species  among 38 Shiga toxin-producing E. coli (STEC) O157 isolates of human and animal origin . Using methods akin to the prediction of AMR and pathogenicity or virulence genes, bioinformatics pipelines can be used to identify the presence of genes known to provide higher ability to grow or survive under stress conditions. As an example, iccation . In the biofilms , the abin cattle  or the aeratures .2 to 103 genes are compared between genomes). The latter is an expansion of the traditional multilocus sequence typing (MLST) approach  and includes whole-, core- and accessory-genome MLST   . The str agMLST) , and it eritidis , L. monoytogenes  and quin. jejuni  showed h. jejuni . It requ. jejuni . As two C. jejuni isolates from poultry in 12 European countries and a set of 536 previously published C. jejuni genomes retrieved from the European Nucleotide Archive, C. jejuni. In other words, they explored whether the emergence of fluoroquinolone-resistant C. jejuni was related to the transmission among countries or to the selection through fluoroquinolone use in individual countries  and New York (NY) in the United States, highlighted several geographic location-specific clones , as well as broadly distributed clonal groups with similar AMR profiles that likely emerged a long time ago and successfully disseminated to wider populations , S. Heidelberg infections in Canada . WGS was successfully used for source attribution of L. monocytogenes in the European Union  or comparative exposure or risk assessments  to point-of-exposure . This can be done using comparative genomics of isolates collected at successive points or stages of the farm-to-fork continuum . Such anCampylobacter, E. coli and Salmonella populations circulating on-farm, at abattoir and at retail for major livestock species or their meat products  clustering with those found at step n. The sensitivity of this probability estimate as a function of the clustering threshold could also be explored. The uncertainty around the probability estimate could be assessed using bootstrapping as proposed by Second, a quantitative outcome describing the probability of a hazard to be transmitted from one step of the food chain to the next is needed. L. monocytogenes in a meat establishment has already been described . Just like any other molecular comparative analyses, direct interpretation of the observed relationships may be hampered by the complexity of the food chain, with food products being increasingly produced, processed, transformed and consumed in different regions or countries and within different timeframes (as influenced by the product shelf life) .As a simple, deterministic example, if WGS analyses of surveillance data revealed that only half of direct hazards in retail beef are genetically related to isolates collected in on-farm cattle, then the probability of the hazard to be transmitted from the farm to retail product would be 50%. This would have profound implications of the evaluation of interventions in the exposure assessment, as interventions modeled on-farm would only have an impact on 50% of the population of the hazard that humans are exposed to in retail beef, in this hypothetical example (assuming that the other 50% of the hazard population sampled in retail beef have a different origin). Therefore, in a purely linear model, any intervention that reduces prevalence of the hazard by 90% on-farm would only reduce overall exposure to humans from beef consumption by 45%.E. coli and from E. coli to environmental bacteria in the food-processing chain) has also been described  to a vancomycin-susceptible E. faecium of human origin in the intestines of three out of six human volunteers (In those cases where risk assessors decide to focus on the AMR gene(s) as the hazard of interest, and if AMR profiling via WGS shows that the AMR gene(s) are located on MGEs , then horizontal transfer of AMR genes should preferably be included in the exposure assessment step of a QMRA. This will contribute to providing a comprehensive depiction of the exposure pathways . Horizonescribed . Plasmid minutes . Followi minutes . For exalunteers .E. coli in the large intestine of cattle) that include horizontal transfer of plasmids between bacteria in the presence or absence of antimicrobial treatment have been developed  which is a critical piece of information, but also help to better characterize those plasmids. For example, WGS can be used to identify the incompatibility group or replicon type of a plasmid, which usually correlates with the dynamics and efficiency of horizontal transfer, as well as the plasmid host-spectrum . WGS canAs part of the hazard characterization step of a foodborne AMR QMRA, risk assessors aim to translate levels of exposure to the hazard into a probability of one or more adverse health outcomes in humans . Wherevein vitro model of the human gastrointestinal tract, S. Heidelberg and S. Typhimurium in the gastrointestinal tract and ability to cause infection was highly variable  before reaching a suitable site for colonization, attachment and invasion that will eventually lead to infection and subsequently to disease . Dose–revariable . WGS provariable .Once the dose is adjusted, the probability of infection (or disease) could further be refined using WGS profiling of the pathogenicity of the hazard, with highly pathogenic strains requiring lower doses to cause infection or disease . HoweverS. Heidelberg in Canada attributable to prior antimicrobial consumption in humans  prior to ingestion of a contaminated product. This refers to the notion of ‘etiologic fraction’ for prior antimicrobial use, defined by the Institute of Medicine as the proportion of human cases that would not occur but for the resistance of the infecting bacterial strain to the antimicrobial(s) being administered to the patient prior to infection . Human un humans . Howevern humans .TEM) gene copies present in food, but there are currently no data to convert a number of gene copies into a probability of infection or illness , and better characterize the expected response (based on pathogenicity and AMR profiles), WGS focuses on the characterization of individual isolates and therefore cannot provide any quantitative estimates of the dose being ingested initially . Other genomic approaches such as metagenomics , is likely to help in this regard. For example, bioinformatic analytical pipelines, such as Resfinder, SEAR or AMRplusplus can be used to quantify the abundance of AMR genes in metagenomes of diverse origin, e.g., animal feces or farm effluents . However illness .Salmonella infections between 1996 and 2006 in the United States revealed significant differences among serovars in terms of case-fatality rates, hospitalization rates and ability to cause invasive disease  to a cluster-based  or a strain-based approach  . Insteadl origin . Expresspic data .E. coli  via WGS makes it possible to predict the phenotypic behavior of a hazard, and therefore to adjust the model parameters accordingly. For example, predicting the hazard’s ability to grow during processing, transport and storage will provide a more accurate estimate of the number of cells ingested at the point of consumption. Consequently, incorporation of WGS data into QMRA models will help reduce the uncertainty in the final risk estimates. Yet, there is some uncertainty in the degree of correlation between genotypic and phenotypic profiles. The detection of genes or genetic markers does not mean this gene or genetic marker will be functional or expressed. Previous literature has shown very high correlation  between AMR WGS and phenotypic profiles among E. coli , Staphyls aureus , Campyloter spp.  and non-enterica . WGS evelmonella . Howevera toxins .S. Enteritidis and S. Typhimurium, both being highly clonal pathogens  following a voluntary withdrawal of ceftiofur use, and an increase in resistance (from 7 to 20%) after reintroduction of ceftiofur use  are unlikely to provide additional information. Comparison of WGS profiles of S. Heidelberg populations over time, however, would enable the assessment of the effect of withdrawing ceftiofur use on the prevalence of ceftiofur-resistant S. Heidelberg in broiler chicken meat in Canada to be refined. For example, WGS could help to describe whether a single bacterial population underwent resurgence after reintroduction of ceftiofur use, or whether the original ceftiofur-resistant S. Heidelberg population died out and a second population took over after ceftiofur use reintroduction.As an example, CIPARS reported that changes in the frequency of isolates resistant to ceftiofur  among chicken ofur use . These oThe integration of WGS data into QMRA modeling represents a critical step toward the development of next generation QMRA of foodborne AMR. Yet, there are a number of challenges that need to be addressed before the transition can be effective. There is still a lack of knowledge translation between bioinformaticians and risk assessors, with bioinformaticians having a limited understanding of the type of information required for QMRA modeling, and risk assessors having poor comprehension of how WGS data are analyzed, including what type of information can be generated and any limitations on these analyses. Risk assessors and epidemiologists need to be better trained in next generation sequencing techniques and molecular epidemiology . Risk as1 combines several free tools for rapidly performing simultaneous analyses of draft Salmonella genome assemblies, including serovar prediction and sequence-based typing analyses , as well as metadata driven visualization to examine the phylogenetic, geospatial and temporal distribution of genome-sequenced isolates in comparison with a database comprising over 4,000 publicly available genomes  are influenced by the microbiota present in food, food environments or gastrointestinal tracts of humans and animals. Yet, metagenomics approaches also come with new challenges. While WGS rely on ‘clean’ genome sequence analyses, metagenomics require ‘noisy’ sequencing efforts where pathogenic bacteria only represent a minute amount of the bacterial community in a sample, so enrichment or removal of DNA from other sources are needed to improve data quality; these procedures introduce significant bias in the interpretation of metagenomics analyses . Additiomcr-1 gene , which codes for plasmid-based resistance to polymyxin in Gram-negative bacteria (polymyxin is one of the few available drugs for treating infections caused by carbapenem-resistant Enterobacteriaceae)  . A novelin China . Whole pa (mcr-1 . The rapntinents , and subxpansion . This exThe integration of WGS data into foodborne AMR QMRA modeling offers the opportunity to move toward the next-generation of AMR risk assessment. Instead of considering all hazard strains as equally able to cause disease, WGS can substantially improve the hazard identification by focusing on those strains of highest public health relevance and/or stratifying the hazards into subsets of similar genetic profiles that are expected to behave similarly. The distribution of QMRA model parameters, including growth/survival and pathogenicity/virulence can be adjusted accordingly, making it possible to capture the variability in the strains of interest while decreasing the uncertainty in some model input parameters. The high discriminatory power of WGS also offers an opportunity to improve the exposure assessment by analyzing the degree of relatedness between bacterial populations and AMR profiles found at successive stages of the food chain. Overall, WGS can contribute to substantial improvements to the accuracy of QMRA models, and should be considered in any future revision of the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR. WGS data, however, also introduce new sources of uncertainty, especially related to the thresholds of relatedness to be used and the degree of correlation between genotypic and phenotypic profiles. The latter could be improved using a functional approach of microbial behavior based on a combination of omics techniques.LC wrote the initial draft of the manuscript. PB, CC, BC, AF, BH, SM, EP, RR-S, ET, and BS provided substantial comments and edits to improve the text and figures and prepare the final version of the article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Michael D. L. Johnson is a molecular microbiologist observing the role of metals in bacterial biology. In this mSphere of Influence article, he discusses the impact that the paper “Decoupling of the minority PhD talent pool and assistant professor hiring in medical school basic science departments in the US” by Kenneth D. Gibbs, Jr., Jacob Basson, Imam M. Xierali, and David A. https://doi.org/10.7554/eLife.21393) had on him regarding hiring underrepresented minorities at research-intensive institutions and what he considers to be the next steps.Michael D. L. Johnson is a molecular microbiologist observing the role of metals in bacterial biology. In this mSphere of Influence article, he discusses the impact that the paper “Decoupling of the minority PhD talent pool and assistant professor hiring in medical school basic science departments in the US” by Kenneth D. Gibbs, Jr., Jacob Basson, Imam M. Xierali, and David A. Broniatowski (eLife 5:e21393, 2016,  As biomedical science departments have grown, a simple question to ask is who has filled those positions? When I look around at other research-intensive institutions, I do not really see a lot of people who look like me peering back. One might assume that it just takes time, that eventually the percentage of underrepresented minorities  in the population will reflect the number of URM Ph.D.’s, and that that number will be reflected in the number of URM professors. This is why the paper “Decoupling of the minority PhD talent pool and assistant professor hiring in medical school basic science departments in the US” from Gibbs et al. in eLife completely and abruptly changed my viewpoint . It is pGiven the sheer numbers of URM STEM Ph.D.’s entering the field, the problem seems to be at the postdoctoral level. Perhaps URM postdoctoral fellows decide in the midst of this training period that they no longer want to be on the academic track. However, the pool of potential URM candidates has gone up 7.6-fold since the 1980s . The sizThis information particularly struck me because as a URM assistant professor at a research-intensive institution, I often wonder two things. The first is, given these statistics, by what miracle did I beat the odds to get here? This question can easily lead to, and often does with me and other URM assistant professors, a bout of imposter syndrome. For me, this syndrome is especially true after looking at how my degree in music and less-than-stellar grades in undergraduate science classes stack up against the field. The second thing, and the action item, is what can I do to get other URMs in a similar position as myself? I have been given the advice for as long as I can remember in science that the best thing I can do for future URMs is to be successful. While that might be the best thing for me and my institution (which are good things in their own right), it is not the best thing for the next generation of potential URMs as, given the above data, my or other URMs’ success as a professor will still not bring about more diversity in the professoriate. Further, it puts undue stress and pressure on young URM scientists, as if to say “succeed or in the future your whole ethnic group will never be considered again” .I see a great number of URM Ph.D.’s who nobly go into positions to support URMs and/or to enhance diversity and inclusion in order to act as that needed counter ion not only for their fellow URM Ph.D.’s to go into the professoriate, but to give them the opportunities and mentoring to go into the career of their choice. They cannot do it alone. A well-known quote goes as follows: “where your treasure is, there your heart will be also.” I would argue that this “treasure” comes in three major forms, money, autonomy, and time. If this problem is truly on the heart of research-intensive institutions, then I implore them to supply the money to buy into the process of hiring more URMs; the autonomy for administrators, deans, and chairs to connect their diversity principles to their hiring practices; and the time to go out and investigate the number of great URM candidates out there who are interested in doing fantastic research. After all, “we are here, we are here, we are here!”


Background: The dopaminergic system has been associated with the progression of Alzheimer's disease. But previous studies found inconsistent results regarding the relationship between Alzheimer's disease and dopamine when looking at dopamine receptor concentrations.Objective: The aim of this review was to synthesize, using a random-effects model of meta-analysis, the link between the dopaminergic system and Alzheimer's disease.Methods: A detailed analysis protocol was registered at the PROSPERO database prior to data extraction (CRD42018110798). Electronic databases of PubMed, Embase, Web of Science, and Psyc-ARTICLES were searched up to December 2018 for studies that examined dopamine and dopamine receptors in relation to Alzheimer's disease. Standardized mean differences (SMD) were calculated to assess group differences in the levels of dopaminergic neurometabolites.Results: Seventeen studies met the eligibility criteria. Collectively, they included 512 patients and 500 healthy controls. There were significantly lower levels of dopamine in patients with Alzheimer's disease compared with controls . In addition, dopamine 1 receptor  and dopamine 2 receptor  levels were decreased in patients with Alzheimer's disease compared with controls. The results of network meta-analysis indicated that the rank of correlation with Alzheimer's disease from highest to lowest was dopamine (0.74), dopamine 2 receptor (0.49), dopamine 3 receptor (0.46), dopamine 4 receptor (0.33), dopamine 5 receptor (0.31), and dopamine 1 receptor (0.64).Conclusions: Overall, decreased levels of dopaminergic neurotransmitters were linked with the pathophysiology of Alzheimer's disease. Nonetheless, there is a clear need for more prospective studies to validate these hypotheses. Alzheimer's disease (AD) is a progressive neurodegenerative disorder with complex etiology . In addition, it followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines :  AND . These search terms were adapted for the other databases whose detailed search strategies are shown in the supplemental material. Hand searching was conducted by XP and AC. Any inconsistencies between them were resolved by group discussion and consensus with a third party, AL.in vitro experiments; and (4) were gray literature .Studies were considered eligible if they (1) were case control studies, included AD cases and healthy controls; (2) described AD diagnostic criteria based on standardized criteria, such as that defined in the DSM or other international standardized criteria; and (3) reported mean and standard deviation (SD) of dopamine and dopamine receptor concentrations. Studies were excluded if they (1) were review articles or case reports; (2) studied AD in combination with other mental illnesses or in vascular dementia patients who used psychotropic medication or other medications which could influence the dopamine and dopamine receptor concentrations; (3) studied non-humans or were For the purpose of the meta-analysis, two independent investigators [XP and AC] extracted the following information according to the inclusion criteria specified above: (1) name of the first author and publication year; (2) country of the study; (3) study characteristics: mean age and standard deviation  of participants, gender distribution of participants, AD assessment method, and dopamine and dopamine receptors measurements comprising type of sample, sample bonder, storage temperatures (the meaning of frozen is whether samples were reported for frozen preservation in the study), and assay methods; and (4) mean and SD of dopamine and dopamine receptor concentrations. All the extracted data were organized in EpiData 3.0 and saved in Excel.The Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the quality of the eligible studies Stang, . Each elI2 statistic and assessed by the Cochran's Q-statistic . First, we performed meta-analysis of all enrolled studies to compare, one at a time, the concentrations of dopamine, D1R, D2R, D3R, D4R, and D5R between AD patients and healthy controls. This comparison was made using the standardized mean difference (SMD) of the foregoing concentrations between these two groups , and heterogeneity was considerable , and with high heterogeneity  (= 0.013) . There w2 = 0%). On the other hand, there were no group differences for samples which were preserved at room temperature.Subgroup analyses for D1R found that D1R concentration levels were lower in patients with AD compared with controls  when the samples were cryopreserved. This result was with no heterogeneity (I2 = 7.80%), when USA studies were considered. Apparently, subgroup analysis of studies with high NOS scores indicated notably lower heterogeneity (I2 = 0%) than when meta-analysis was performed for all the studies together. Also, in the subgroup analysis by gender, it was observed that females with AD presented with lower concentration levels of D2R than female controls , but no such significant difference was apparent between males. Finally, significantly lower D2R concentration levels were also observed in AD patients compared to the controls when receptor autoradiography was used in the assay of D2R.Additionally, D2R concentration levels were significantly lower in AD patients than in healthy controls , with low heterogeneity  may be associated with the process of AD pathophysiology progression  dopaminergic neurons, modulates and projects mainly cerebral cortex, nucleus accumbens and hippocampal functions, including control of voluntary movement, memory and synaptic plasticity  and central cholinergic transmission (restored) in AD patients . For example, Barnum described a spectrophotometric method for determining dopamine, catechol, epinephrine and other aromatic vicdiols in 1977 . In addition Ehrin et al.  homodimer and G protein provides a main functional unit and has emerged as potential novel targets for drug development (Casadó-Anguera et al., Relatively, network meta-analysis indicated that the rank of correlation between the six treatments and AD was in the following descending order of the strength of the correlation: dopamine, D2R, D3R, D4R, D5R, and D1R. These results are consistent with previous studies on dopaminergic system in patients with AD. Moreover, it is known that by acting through the D2-like receptors, dopamine increases cortical excitability, while via D1-like receptors it increases cortical acetylcholine release. This result may imply that D2-like receptors play a more important role in the development of AD than D1-like receptors (Hagena and Manahan-Vaughan, This hypothesis has been supported by recent research, since medications intended to treat acetylcholine deficiency have not been very effective for AD. In addition, these findings are corroborated by clinical studies, suggesting that stimulation of primary motor cortex by TMS in AD patients revealed an impairment of central cholinergic activity that can be transiently restored by the administration of Levodopa or rotigotine, or a dopamine D2-like agonist (Martorana et al., First, each eligible study for this meta-analysis had a small sample size, perhaps due to the need for advanced technology costs in order to perform such studies successfully. The small sample sizes of the eligible studies might have compromised the significance of the conclusions (Sweet et al., Our meta-analysis has shown that there is evidence to suggest that dopamine, D1R and D2R concentration levels were decreased in patients with AD compared with controls. In addition, decreased levels of dopamine and D2-like receptors were linked with the pathophysiology of AD because of their strong higher rank correlations with AD. Nonetheless, there is a clear need for more prospective studies to validate these hypotheses.XP and AL contributed to the study design, while XP and AK contributed to the data collection. Statistical analyses and interpretation of results were performed by XP and AK, while XP, XW, KA, AL, and SW drafted the manuscript and edited the language. All the authors participated in the critical revision and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics on Twitter are typically derived in the form of (a) frequently used hashtags, or (b) keywords showing sudden trends of large occurrence in a short span of time (“bursty keywords”), or (c) concepts latent within the tweets that are grouped using variations of semantic clustering techniques.In the current paper, we jointly model the hashtags present and the semantic concepts embedded in the content, which in turn helps us identify hashtag groups that define a “topic”—a concept space—that are used by a large number of tweets.We observe that different hashtags belonging to a given cluster are more prominent compared to the others, at different times. We further observe that the participation and influence levels of the different users play important roles in determining which hashtag would be more prominent than the others at given times. We thus observe topics to often morph from one to the other (via morphing of dominant hashtags representing the same semantic concept space), rather than becoming extinct outright, which is a novel insight about topic lifecycles. We further present novel observations about the role of users in determining the lifecycle of discussion topics on Twitter.We infer that topic lifecycles are governed by user interests, and not by user influence, which is a key observation made by our work. Twitter has emerged as one of the most popular social network platforms with plethora of user-generated content, and one of the most explored ones by researchers. Works have attempted to characterize information diffusion, as well as to understand the dynamics of information diffusion over the social networks, along topics of discussion. Several models for information diffusion as well as methods to identify topics have been proposed in the literature.pre-growth (birth) phase, growing through the growth phase to reach the peak phase, and then eventually undergoing through the decay phase and dying down, around and after which a final post phase takes place. Other prior work in the literature has inspected Twitter hashtag lifecycle shape also. Using their K-spectral centroid (KSC) algorithm, Yang and Leskovec [A work by Ardon et al.  performeLeskovec  characteLeskovec . While twhat is the impact of users in determining the lifecycle of topics? In addition, there remains a scope to improve the definition of topics, while using a semantic-temporal definition framework. All of these would lead to significant novel insights, that have not been addressed in the earlier literature, thus motivating the current work.Clearly, the research of topic lifecycle on Twitter requires attaining further maturity. We recently performed a preliminary work to explore whether topic lifecycles can be better modeled and understood on Twitter, and reported our preliminary findings in Dey et al. . HoweverOverall, this work explores evolution within topics, wherein one hashtag central to the topic is replaced by another, while users discuss a topic, on Twitter. Since the Twitter topic lifecycle literature simply treats each hashtag as a topic and assumes that a topic is past when a single hashtag is not used any more (or used very little), this study re-discovers the lifecycle of topics.In this work, as part of topic detection of tweets, we perform semantic clustering of topics using word-embedding techniques. This approach is different from our earlier work , where tOur renewed definition of topics enables us to analyze their lifecycle in a manner different from the rest of the literature, and we obtain novel insights. One of our key hypothesis is that, topics do not die; instead, they morph from one primary hashtag to another , and such evolution of the primary hashtag keeps happening for some time before the topic really dies down or morphs away into a different topic. A second key hypothesis is that, such evolution is enabled by the social mass participating in the topic, not by some highly influential single user. And further, we also hypothesize that the lifecycle of topics is correlated with the distribution of social connections. We explore real-life Twitter data, conducting experiments driven by the hypothesis above. Our experiments indicate that topic lifecycles are governed by user interests, and not by user influence, which is a first-of-its-kind insight in the space of topic lifecycle analysis on Twitter.The rest of this paper is organized as follows. "The literature for topic lifecycle analysis involves works around (a) identifying topics, (b) identifying topic characteristics for given periods of time, and (c) identifying the social association of the topic.#olympics, #olympicgames, and #theolympicgames, all intend to address the same topic. Our work addresses this shortcoming, as one of its key contribution.For detecting topics on Twitter, three techniques have been prominent in the literature. In the first, each hashtag is treated as a topic . In the pre-growth (birth) phase, growing through the growth phase to reach the peak phase, and then eventually undergoing through the decay phase and dying down, around and after which a final post-phase takes place. They augment their work by adding entities and places as hashtags, since a significant volume of tweets does not bear hashtags. Among other works, using their K-spectral centroid (KSC) algorithm, Yang and Leskovec [Ardon et al.  conduct Leskovec  characteLeskovec , consideTwitter information diffusion is a well-studied area in general –15. DiscWe presented a preliminary version of our findings in an initial report ; howeverAs described in "We identify conceptually  related hashtags using the “average word-embedding” of hashtags, as a simple average of embeddings of the words present in the group of tweets under consideration. We do this as a two-step process. First, for each hashtag present in the data, a document gets created. Second, for each hashtag, a word-embedding is created from its corresponding document. In principle, this approach is akin to Dey et al. .Let ar GloVe ), we lock-means clustering based upon embedding distances of hashtag pairs, to segregate the hashtags into semantically related clusters. We apply temporal thresholds to create different groups for the different hashtags that do not get used  around similar times.We perform semantic-temporal clustering process to create semantically related clusters of hashtags that occur temporally close enough. We perform k-means clustering approach is used for identifying hashtag clusters. The distance between a given pair of hashtags is defined as the embedding distance of the two vectors representing the two respective hashtags. Embedding distance can be computed as any well-known distance function of a pair of vectors; in our experiments, we use cosine similarity of the embeddings of the hashtag pair, such that the higher the cosine similarity between the pair of hashtags, the lower is the distance. Formally, the cosine similarity between a d-dimensional vector pair u and v is computed ask-means clustering on the hashtags, thereby producing semantically related hashtag clusters, As indicated earlier, a overlap relationship denotes instances, where there exists some (non-zero) co-occurrence of a given pair of events, the meets relationship captures, where one event starts as soon as another one stops, and the disjoint relationship represents when one event starts after another one is over with no common point of occurrence in time.The clusters generated by the semantic relationship building process ensures creating clusters of hashtags that have been used in tweets with semantic similarity. However, it is important to ensure that the hashtags are also clustered such that they co-occur temporally, within permissible time periods, so that the clusters only contain hashtags that can lead to a potential continuity of discussion topics . We apply Allen’s temporal functions  on the soverlaps or meets relationships, or if they are disjoint but not by more than a threshold time period . Furthermore, the pair is also said to be related if at least another hashtag overlaps, or meets, or, within a threshold, disjoint). This makes the temporal relationship recurrent in nature. Furthermore, note that a pair of hashtags overlaps, meets, or, within a permissible threshold, disjoint relationship. Formally, the temporal relationship is given astemporally related and overlaps relationships, respectively. A semantic cluster m different temporal relationships will be correspondingly split into m clusters, namely, The time series for the individual hashtags, as well as the semantic clusters of the hashtags derived earlier, are developed. We compute whether a hashtag, as well as some hashtag(s) from its cluster, was used (or not) in a given time slot. A semantically related hashtag pair The topics are defined as semantic-temporally related hashtag clusters, and are computationally finalized as clusters of hashtags that are related semantically. As an intuitive example, we expect the hashtags to be clustered together as long at they occur closely enough in time , such as we expect a cluster to contain hashtags such as {#clinton, #hilaryclinton, #clintonelection2016}, and a different cluster to contain a different group of hashtags altogether, such as {#bobdylan, #johndenver, #harrybelafonte}.pre-growth (birth) phase, where the topic is born in  the network, grows through the growth phase to reach the peak phase, where “early majority” discuss it, undergo the decay phase (where ‘late majority” discuss it) and die down, around and after which a final post-phase takes place, where “laggards” discuss it. The literature treats given hashtags, as well as hashtags assigned via natural language processing (NLP) techniques, as topics.Prior literature  indicateOur work, though, only uses the tweets with given hashtags, especially given the relatively low state-of-the-art accuracy in the space of hashtag prediction on Twitter , since pc count the number of uses of a hashtag within a given time slot in cluster Let a given topic cluster mentclass2pt{minimc is the counting function for the number of uses of each hashtag If for a given topic cluster u makes a tweet having hashtag u is up-counted. The process is repeated for all users, across all hashtags. If a hashtag contains multiple tweets belonging to the same topic at the same time slot, then the user’s participation in the topic at that time slot is multiply counted. This completes the creation of user timeline for topic participation.We further create user timelines for their participation in topics, and analyze their roles in determining the topic lifecycles. We determine the participation of each user in a topic, by identifying whether they post tweets that contain at least one hashtag that is a part of the topic’s hashtag cluster. For each time slot, we find the participation of each user in a given topic. If user n users d is a damping factor that avoids transforming the entire Page Rank of a user to its neighbors. We further perform hashtag usage profiling of users for each time slot, by calculating the total number of users that (a) use only the dominating hashtag, (b) use both dominating and non-dominating hashtags, and (c) use only non-dominating hashtags. We correlate these hashtag usage profiles of the users with their Page Ranks, to obtain an overall understanding of the role and influence of users towards the dominance (and non-dominance) of given hashtags at given slots of time. We perform this study over the entire lifecycle of given hashtag clusters (topics) as well as individual hashtags, to obtain a complete understanding of the role of users in the topic lifecycles.Using the user timeline created for their participation in the topics at each of the time slots, we proceed to assess the roles they play in the topic lifecycles. We determine the influence of the individual users in the overall graph, using their social friendship connections . We use the well-known Page Rank  to measuWe perform two types of characterizations of the social network graph with respect to the topic lifecycles. In the first, we examine the graph-level metrics of the topic graphs, such as connected components (strong and weak), degree distribution and graph diameter. In the second, we examine the user-level characteristics with respect to the topic graphs, such as page rank and user participation distributions for the topics. These are well-known graph metrics, and we assume prior familiarity of the reader to these metrics.We perform our experiments using the models developed in "We use the Twitter data set by Yang et al.  at Stanfk-means clustering process, since there is no well-agreed upon way to determine a universally good value of k in the literature. For the June data, we detect, respectively, 50, 100, 150, ..., 450 and 500 topics . For July, we detect 200, 400, 600, ..., 1800 and 2000 topics , respectively. In other words, to maintain consistency of observations, we experiment over 10 different clustering granularities that range approximately from slightly higher than 2.5% of the data size, leading to slightly less than 40 hashtags per cluster on an average , to slightly higher than 25% of the data size, leading to slightly less than 4 hashtags per cluster on an average . This is followed by timeline creation for individual hashtags, for topics (hashtag clusters) as a whole and for users. We set the duration of each time slot as 1 h. This sets up the platform for performing the main experiments.We perform the hashtag clustering using the semantic and temporal techniques, as disclosed in "Table The lifecycle of a topic is characterized by its different intensities and different dominant hashtags, over its period of existence. We explore the topic intensity characteristics over the time period, in Fig. We further explore the hashtag dominance patterns over time, across different hashtags. We illustrate the patterns in Fig. To provide a deeper level of intuition to the reader over a longer period, we pick a random hashtag from the hashtags that span across the 2 months, June and July, and show that while individual hashtags do not last long , the hashtag cluster lasts much longer, and morphs from one hashtag to another. This phenomenon has been captured in Fig. #votonulo. This hashtag is born around 23rd June 2009 and dies around 11th July 2009. It connects the hashtags #CrisisHN, #noticias100 and #urna2009 in June 2009, and #votomx and #CrisisHN in July 2009, thus semantically connecting the evolving concepts. Note that, the dominating hashtag in June 2009 for this cluster has primarily been #noticias100 and #urna2009 and in July 2009 has been #votomx, while #votonulo and #CrisisHN have been non-dominant. We further observe in the data that the hashtags #noticias100 and #urna2009 do not even exist in July 2009, having died by then, and the hashtag #votomx does not exist in June, getting born in early July 2009. This example thus provides the reader a demonstration of how the hashtags undergo birth and death, but semantic topics continue for longer than the lifespan of the topic.As an example of a hashtag that spans across 2 months of our data set, we randomly select the hashtag interested users drive topic lifecycles.Figure influence of users do not play a significant role in determining the course topic lifecycles on Twitter. Combining with the earlier observations, we make the following key inference: topic lifecycles in Twitter are driven by user’s interests, and not by their influence , and retaining their social edges. Conversely, we construct another induced subgraph, by selecting all the users that have used only non-dominant hashtags in at least one time slot (and not used the dominant hashtag in that time slot), and retaining their social edges. We find the average degrees of the pairs of the induced subgraphs, and plot that against the number of participating nodes in the respective subgraphs, in Fig. In the following subsection, we explore the characteristics of the overall graph, based upon its social connection properties.We perform a graph-level characteristic study, constructing an induced subgraphs for each topic, from the original social connections graph. An edge is drawn in the induced subgraph if there is a social edge between a pair of users. Effectively, this gives rise to a topic lifecycle-specific network subgraph. Thus, the graph gets constructed. The input data are characterized by an exploration of the degree distributions, as shown in Fig. We determine the graph diameter, as well as the connected components, both the strongly connected components and weakly connected ones, within this subgraph, which are shown in Figs. topics morph over time using semantically related hashtags, over socially well-connected graphs, driven by user interests, but without much effect of user influence.Overall, combined with the observations and inferences made at all the earlier stages of this paper, we infer that, Please note that, we use the Python “NetworkX” package to compute all the graph properties.Multiple techniques exist in the literature for finding topics on Twitter. These include simple hashtag-based models , burst-oOur work elicits a first-of-its-kind observation in the space of Twitter topic lifecycle analysis—the lifecycle and hashtag morphing are an effect of user interest, not influence. We observe the user influences (captured by Page Rank) to be a mixture of the dominant and other hashtags, where a dominant hashtag is defined as the most prominent hashtag of a topic at a given time slot. On the other hand, the dominant hashtags are always produced by a relatively much smaller number of users participating in the topic, while the rest of the users, although larger in number, tend to spread their usage over other hashtags. Dominant hashtags change with time as part of the evolution of the topic over hashtags; however, the characteristic of a topic (hashtag cluster) to be governed primarily by a small number of interested users, instead of a large number of users or users with high influence, remains unchanged. This is one of the key takeaways of the current paper.topics morph over time using semantically related hashtags, over socially well-connected graphs, driven by user interests, but without much effect of user influence. This is a novel insight proposed by our work. In the future, it will be interesting to investigate whether any distinction exists between topics that are spontaneously born within social networks, versus topics that emerge out of influence .Overall, we infer that In this work, we modeled the lifecycle of topics on Twitter, using a model of topic detection that has not been used in this context earlier in the literature. The topic is determined as a cluster of hashtag, where the clustering is carried out using semantic similarity of containing tweets and temporal occurrence of those tweets. In the process, we observed how topics morph from one to the other over time, on multiple real-life data sets, over varying intensities of presence. We also observed how at different points of time, different hashtags dominate the hashtag (topic) clusters. In the process, we assessed the influence of the users using the different hashtags, for the dominating as well as the non-dominating hashtags for each cluster. We observed the power of the masses over individual influence in the social network settings across the data sets—the hashtags used by the majority tends to dominate the hashtag clusters, and the hashtags used by the influential users are seen to be non-dominant. Our work is the first of its kind, that analyzes the roles of users in shaping up the lifecycle of topics, and determining whether or not topics morph over time via hashtags or die down. We infer that topic lifecycles are governed by user interests, and not by user influence. The work can be used in social marketing and information spread modeling applications. In the future, we propose to use different measures of user influence as well as different semantic similarity and temporal thresholds, to refine the empirical understanding and obtain an improved solution.


A novel fluorescent reporter HKYellow was rationally designed for the robust visualization of peroxynitrite production in live cells and tissues. N-phenyl group to the rhodamine core and a peroxynitrite-triggered N-dearylation reaction to achieve a sensitive and selective fluorescence turn-on response toward peroxynitrite in chemical and biological levels. This probe has been thoroughly evaluated for the robust imaging of peroxynitrite in live cells and tissues. By utilizing HKYellow, we provide the first visual evidence that peroxynitrite is generated in mouse liver tissues under an acute alcohol binge or ischemic–reperfusion condition. This probe should be a powerful molecular imaging tool for interrogating the complex biological roles of peroxynitrite under various biological settings.Herein, we present the rational design, synthesis, characterization, and biological applications of a new rhodamine-based fluorescent probe, HKYellow, for the detection and molecular imaging of peroxynitrite, an important highly reactive oxidant involved in a variety of physiological and pathological processes. HKYellow was rationally designed on the basis of the efficient fluorescence quenching effect of the  As a strong oxidant, peroxynitrite interacts with a panel of biomolecules, including lipids, DNA, and proteins, via direct oxidation or indirect secondary radical-mediated reactions and causes serious irreversible damage to them.2˙– and ˙NO. As a result, peroxynitrite generation is thought to represent an important pathogenic mechanism in a series of diseases such as neurodegeneration, ischemia–reperfusion injury, inflammation, vascular diseases, circulatory shock, and cancer.Reactive oxygen species (ROS) and reactive nitrogen species (RNS) refer to oxygen-containing and nitrogen-containing molecules with a high reactivity to biomolecules, respectively. They are involved in a wide variety of biological events, including oxidative damage, cellular signalling, and immune response.e.g., tyrosine nitration, which inevitably results in doubts and debates about its biological relevance.in situ detection, spatial and temporal imaging should be powerful tools for directly monitoring peroxynitrite generation and interrogating its biological roles.Most of the current studies on peroxynitrite rely on indirect measurements and its molecular footprints, Previously, we reported a series of green fluorescent probes for peroxynitrite detection and imaging.N-dearylation reaction of N-(4-hydroxylphenyl)-rhodol exerted by peroxynitrite.i.e., the rhodol part and the N-phenyl group -2,2,4-trimethyl-tetrahydroquinoline moiety was incorporated into the probe . HKYellow is readily soluble in water, exhibiting a strong absorption peak at 560 nm , such as HOCl and ˙OH,The reactivity of HKYellow toward a panel of ROS and RNS was also assessed to evaluate the selectivity of the probe for detecting peroxynitrite . As expe2O2, NO donor NOC-18, or O2˙– donor MSB , and broad biological applicability. These features are highly desirable for applying HKYellow to the molecular imaging of peroxynitrite in live cells and tissues. Indeed, taking advantage of this new probe, we directly visualized endogenous peroxynitrite production in live cells under OGD/RO conditions or upon treatment with excitatory glutamate. More importantly, HKYellow has been extensively utilized for molecular imaging of peroxynitrite in live mouse liver tissues, enabling direct visualization of endogenous peroxynitrite generation in mouse livers under acute alcohol binge or ischemic–reperfusion conditions for the first time and therefore providing new insights into the pathological mechanisms of these two important disease conditions. Given its robustness and versatileness, HKYellow should also serve as a powerful molecular imaging tool for studying the biology of peroxynitrite under various physiological and pathological contexts in the future. Moreover, the rational design strategy for HKYellow based on the fluorescence quenching effect of the N-phenyl group to xanthene fluorophores and a peroxynitrite-triggered N-dearylation reaction can be generalized to other core fluorophores to further expand our toolbox of peroxynitrite probes, which will be reported in due course.To summarize, we presented the rational development of a robust fluorescent probe HKYellow for molecular imaging of peroxynitrite utilizing a peroxynitrite-induced Supplementary informationClick here for additional data file.


Phage reproduction depends on bacterial host cells, and thus it is critical for temperate phages to modulate their reproduction strategy in response to host cell densities. Silpe and Bassler  elucidatE. coli system. A model bacterial system was used to reveal the basis of the molecular regulatory mechanism  independent. Many studies have also shown that phage abundance is positively correlated with host density in a variety of environments  inducing lytic reproduction of VP882 phages. Silpe and Bassler  for cell-cell communication to coordinate a wide range of group behaviors; a process called quorum sensing (QS) that is cell density dependent. Papenfort et al.  recently Bassler  demonstrfe cycle . This giphage-mediated lysis, the authors showed that Q production occurs first and thereafter Q activates gp69 launching the lysis program. However, further investigation revealed that the pathway of VqmAphage-mediated lysis is distinct from that of MMC-induced phage lysis. The DPO-activated phage-produced VqmAphage binds upstream of phage gene gp55 activating its expression that leads to the anti-repressor Q-tip production. The function of the 79-amino acid protein Q-tip is to directly aggregate the cl repressor and prevent cl from binding to the promoter of q which allows Q production inducing lysis (Silpe and Bassler  went on ng lysis . SpecifiSerratia cell density can promote CRISPR-Cas immune defense through acyl-homoserine lactone-based communication, whereas many phages also encode CRISPR-Cas inhibitor proteins to fight back (Rauch et al., QS is also involved in other phage-bacteria interactions. Patterson et al.  discoverBacillus phages within the subtilis phage beta group can also sense the population level of nearby relatives and strongly influence the lysis-lysogeny decisions through phage-encoded peptides (Erez et al., Vibrio phage system report by Silpe and Bassler ( Bassler  in whichphage-Q-tip-like lysis-lysogeny regulation pathway, Silpe and Bassler (phage-Q-tip were identified in four phage genomes, and none of the above DNA binding proteins were VqmAphage homologs leading the authors to speculate that these phage-originated proteins respond to different host-produced signals to initiate transcription of anti-repressor-encoding genes. Intrigued by these discoveries, we propose a complementary hypothesis targeted at the microbial ecosystem level. Because quorum-sensing signals act in a rather taxon-specific manner, we hypothesize that any single quorum-sensing signal should only induce prophages within a small subset of closely-related host bacteria within vastly more diverse microbial communities.In addressing the generality of vqmA Bassler  found thXL proposed the idea and drafted the article. MR helped XL develop the idea and improve the article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.