Dielectric response of modified Hubbard models with neutral-ionic and Peierls transitions

The dipole P(F) of systems with periodic boundary conditions (PBC) in a static electric field F is applied to one-dimensional Peierls-Hubbard models for organic charge-transfer (CT) salts. Exact results for P(F) are obtained for finite systems of N = 14 and 16 sites that are almost converged to infinite chains in deformable lattices subject to a Peierls transition. The electronic polarizability per site, \alpha_{el} = (\partial P/\partial F)_0, of rigid stacks with alternating transfer integrals t(1 +/- \delta) diverges at the neutral-ionic transition for \delta = 0 but remains finite for \delta > 0 in dimerized chains. The Peierls or dimerization mode couples to charge fluctuations along the stack and results in large vibrational contributions, \alpha_{vib}, that are related to \partial P/\partial \delta and that peak sharply at the Peierls transition. The extension of P(F) to correlated electronic states yields the dielectric response \kappa of models with neutral-ionic or Peierls transitions, where \kappa peaks >100 are found with parameters used previously for variable ionicity \rho and vibrational spectra of CT salts. The calculated \kappa accounts for the dielectric response of CT salts based on substituted TTFs (tetrathiafulvalene) and substituted CAs (chloranil). The role of lattice stiffness appears clearly in models: soft systems have a Peierls instability at small \rho and continuous crossover to large \rho, while stiff stacks such as TTF-CA have a first-order transition with discontinuous \rho that is both a neutral-ionic and Peierls transition. The transitions are associated with tuning the electronic ground state of insulators via temperature or pressure in experiments, or via model parameters in calculations.Comment: 10 pages, 9 figures; J.Chem.Phys., in pres

Activity-dependent plasticity of transmitter release from nerve terminals in rat fast and slow muscles

Available under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported license.Peer reviewedPublisher PD

The Influence of Formulation, Buffering, pH and Divalent Cations on the Activity of Endothall on Hydrilla.

Endothall has been used as an aquatic herbicide for more than 40 years and provides very effective weed control of many weeds. Early research regarding the mechanism-of-action of endothall contradicts the symptomology normally associated with the product. Recent studies suggest endothall is a respiratory toxin but the mechanism-of-action remains unknown. To further elucidate the activity of endothall, several endothall formulations were evaluated for their effects on ion leakage, oxygen consumption and photosynthetic oxygen evolution from hydrilla shoot tips. The influence of pH, buffering and divalent cations was also evaluated. (PDF contains 6 pages.

Charge fluctuations and electron-phonon coupling in organic charge-transfer salts with neutral-ionic and Peierls transitions

The first-order transition of the charge-transfer complex TTF-CA (tetrathiafulvalene-chloranil) is both a neutral-ionic and a Peierls transition. In related organic charge transfer complexes, cooling at ambient pressure increases the ionicity $\rho$ in strikingly different ways, and is sometimes accompanied by a dielectric peak, that we relate to lattice stiffness, to structural and energetic disorder, and to the softening of the Peierls mode in the far-IR. The position operator $P$ for systems with periodic boundary conditions makes possible a systematic treatment of electron-phonon interactions in extended donor-acceptor stacks in terms of correlated Peierls-Hubbard models. The IR intensity of the Peierls mode peaks at the Peierls transition at small $\rho < 1/2$ in soft lattices, where the dielectric constant also has a large peak. In dimerized stacks, the IR intensity of totally symmetric, Raman active, molecular vibrations is related to charge fluctuations that modulate site energies. Combination bands of molecular and Peierls modes are identified in regular TTF-CA stacks above Tc. Energetic disorder can suppress the Peierls transition and rationalize a continuous crossover from small to large $\rho$. The TTF-CA scenario of a neutral-regular to ionic-dimerized transition must be broadened considerably in view of charge transfer salts that dimerize on the neutral side, that become ionic without a structural change, or that show vibrational evidence for dimerization at constant $\rho < 1$.Comment: 26 pages including figure

Effect of dactylaria higginsii on purple nutsedge (Cyperus rotundus) interference with pepper (Capsicum annuum L)

Greenhouse studies were conducted to evaluate the effect of the fungal pathogen, Dactylaria higginsii, on purple nutsedge interference with 'Capistrano' pepper (Capsicum annuum). Purple nutsedge plants established from tubers were planted at initial densities of 40, 80,160, and 320 plants m-2with pepper in 35-cm diam pots with a commercial potting medium, under nonlimiting fertilization and irrigation conditions. Three to four-leafstage purple nutsedge and four-leafstage pepper plants were inoculated by spraying D. higginsii in 0.5% Metamucil, a carrier; the treatments were carrier only, 104 conidia ml1 carrier, or 106 conidia conidia ml1 carrier. Purple nutsedge at all tuber densities significantly reduced pepper yield in the absence of D. higginsii. Percentage yield loss of pepper was greater in treatment with 104 conidia ml1• However, percentage yield loss of pepper was negligible in treatments with D. higginsii at 106 conidia ml1 when compared to the non-weedy control. The disease progress rate was significantly faster in treatments with 1(1 conidia ml1 (rc = 0.113 - 0.123) compared to 104 conidia mlJ (rc = 0.049 - 0.050). At 106 conidia ml1 , D. higginsii reduced nutsedge interference, provided greater nutsedge control, and increase pepper yield compared to weedy checks

Factors Affecting Patients’ Use of Electronic Personal Health Records in England: Cross-Sectional Study

Background: Electronic personal health records (ePHRs) are secure Web-based tools that enable individuals to access, manage, and share their medical records. England recently introduced a nationwide ePHR called Patient Online. As with ePHRs in other countries, adoption rates of Patient Online remain low. Understanding factors affecting patients’ ePHR use is important to increase adoption rates and improve the implementation success of ePHRs. Objective: This study aimed to examine factors associated with patients’ use of ePHRs in England. Methods: The unified theory of acceptance and use of technology was adapted to the use of ePHRs. To empirically examine the adapted model, a cross-sectional survey of a convenience sample was carried out in 4 general practices in West Yorkshire, England. Factors associated with the use of ePHRs were explored using structural equation modeling. Results: Of 800 eligible patients invited to take part in the survey, 624 (78.0%) returned a valid questionnaire. Behavioral intention (BI) was significantly influenced by performance expectancy (PE; beta=.57, P<.001), effort expectancy (EE; beta=.16, P<.001), and perceived privacy and security (PPS; beta=.24, P<.001). The path from social influence to BI was not significant (beta=.03, P=.18). Facilitating conditions (FC) and BI significantly influenced use behavior (UB; beta=.25, P<.001 and beta=.53, P<.001, respectively). PE significantly mediated the effect of EE and PPS on BI (beta=.19, P<.001 and beta=.28, P=.001, respectively). Age significantly moderated 3 paths: PE→BI, EE→BI, and FC→UB. Sex significantly moderated only the relationship between PE and BI. A total of 2 paths were significantly moderated by education and internet access: EE→BI and FC→UB. Income moderated the relationship between FC and UB. The adapted model accounted for 51% of the variance in PE, 76% of the variance in BI, and 48% of the variance in UB. Conclusions: This study identified the main factors that affect patients’ use of ePHRs in England, which should be taken into account for the successful implementation of these systems. For example, developers of ePHRs should involve patients in the process of designing the system to consider functions and features that fit patients’ preferences and skills to ensure systems are useful and easy to use. The proposed model accounted for 48% of the variance in UB, indicating the existence of other, as yet unidentified, factors that influence the adoption of ePHRs. Future studies should confirm the effect of the factors included in this model and identify additional factors

Factors that affect the use of electronic personal health records among patients: A systematic review

Background: Electronic personal health records (ePHRs) are web-based tools that enable patients to access parts of their medical records and other services. In spite of the potential benefits of using ePHRs, their adoption rates remain very low. The lack of use of ePHRs among patients leads to implementation failures of these systems. Many studies have been conducted to examine the factors that influence patients’ use of ePHRs, and they need to be synthesised in a meaningful way.ObjectiveThe current study aimed to systematically review the evidence regarding factors that influence patients’ use of ePHRs. Methods: The search included: 42 bibliographic databases (e.g. Medline, Embase, CINHAL, and PsycINFO), hand searching, checking reference lists of the included studies and relevant reviews, contacting experts, and searching two general web engines. Study selection, data extraction, and study quality assessment were carried out by two reviewers independently. The quality of studies was appraised using the Mixed Methods Appraisal Tool. The extracted data were synthesised narratively according to the outcome: intention to use, subjective measures of use, and objective measures of use. The identified factors were categorised into groups based on Or and Karsh’s conceptual framework. Results: Of 5225 citations retrieved, 97 studies were relevant to this review. These studies examined more than 150 different factors: 59 related to intention to use, 52 regarding subjectively-measured use, and 105 related to objectively-measured use. The current review was able to draw definitive conclusions regarding the effect of only 18 factors. Of these, only three factors have been investigated in connection with every outcome, which are: perceived usefulness, privacy and security concerns, and internet access. Conclusion: Of the numerous factors examined by the included studies, this review concluded the effect of 18 factors: 13 personal factors (e.g. gender, ethnicity, and income), four human-technology factors (e.g. perceived usefulness and ease of use), and one organisational factor (facilitating conditions). These factors should be taken into account by stakeholders for the successful implementation of these systems. For example, patients should be assured that the system is secure and no one can access their records without their permission in order to decrease their concerns about the privacy and security. Further, advertising campaigns should be carried out to increase patients’ awareness of the system. More studies are needed to conclude the effect of other factors. In addition, researchers should conduct more theory-based longitudinal studies for assessing factors affecting initial use and continuing use of ePHRs among patients

An overview of the features of chatbots in mental health: A scoping review

Background: Chatbots are systems that are able to converse and interact with human users using spoken, written, and visual languages. Chatbots have the potential to be useful tools for individuals with mental disorders, especially those who are reluctant to seek mental health advice due to stigmatization. While numerous studies have been conducted about using chatbots for mental health, there is a need to systematically bring this evidence together in order to inform mental health providers and potential users about the main features of chatbots and their potential uses, and to inform future research about the main gaps of the previous literature. Objective: We aimed to provide an overview of the features of chatbots used by individuals for their mental health as reported in the empirical literature. Methods: Seven bibliographic databases (Medline, Embase, PsycINFO, Cochrane Central Register of Controlled Trials, IEEE Xplore, ACM Digital Library, and Google Scholar) were used in our search. In addition, backward and forward reference list checking of the included studies and relevant reviews was conducted. Study selection and data extraction were carried out by two reviewers independently. Extracted data were synthesised using a narrative approach. Chatbots were classified according to their purposes, platforms, response generation, dialogue initiative, input and output modalities, embodiment, and targeted disorders. Results: Of 1039 citations retrieved, 53 unique studies were included in this review. Those studies assessed 41 different chatbots. Common uses of chatbots were: therapy (n = 17), training (n = 12), and screening (n = 10). Chatbots in most studies were rule-based (n = 49) and implemented in stand-alone software (n = 37). In 46 studies, chatbots controlled and led the conversations. While the most frequently used input modality was writing language only (n = 26), the most frequently used output modality was a combination of written, spoken and visual languages (n = 28). In the majority of studies, chatbots included virtual representations (n = 44). The most common focus of chatbots was depression (n = 16) or autism (n = 10). Conclusion: Research regarding chatbots in mental health is nascent. There are numerous chatbots that are used for various mental disorders and purposes. Healthcare providers should compare chatbots found in this review to help guide potential users to the most appropriate chatbot to support their mental health needs. More reviews are needed to summarise the evidence regarding the effectiveness and acceptability of chatbots in mental health

Managing disease outbreaks: The importance of vector mobility and spatially heterogeneous control

This work is licensed under a Creative Commons Attribution 4.0 International License.Management strategies for control of vector-borne diseases, for example Zika or dengue, include using larvicide and/or adulticide, either through large-scale application by truck or plane or through door-to-door efforts that require obtaining permission to access private property and spray yards. The efficacy of the latter strategy is highly dependent on the compliance of local residents. Here we develop a model for vector-borne disease transmission between mosquitoes and humans in a neighborhood setting, considering a network of houses connected via nearest-neighbor mosquito movement. We incorporate large-scale application of adulticide via aerial spraying through a uniform increase in vector death rates in all sites, and door-to-door application of larval source reduction and adulticide through a decrease in vector emergence rates and an increase in vector death rates in compliant sites only, where control efficacies are directly connected to real-world experimentally measurable control parameters, application frequencies, and control costs. To develop mechanistic insight into the influence of vector motion and compliance clustering on disease controllability, we determine the basic reproduction number R0 for the system, provide analytic results for the extreme cases of no mosquito movement, infinite hopping rates, and utilize degenerate perturbation theory for the case of slow but non-zero hopping rates. We then determine the application frequencies required for each strategy (alone and combined) in order to reduce R0 to unity, along with the associated costs. Cost-optimal strategies are found to depend strongly on mosquito hopping rates, levels of door-to-door compliance, and spatial clustering of compliant houses, and can include aerial spray alone, door-to-door treatment alone, or a combination of both. The optimization scheme developed here provides a flexible tool for disease management planners which translates modeling results into actionable control advice adaptable to system-specific details.Simons Foundation (426126)University of Kansas General Research Grant (2301-2105075)Department of Defense SERDP contract (W912HQ-16-C-0054