Proteins acting as molecular machines can undergo cyclic internal conformational motions that are coupled to ligand binding and dissociation events. In contrast to their macroscopic counterparts, nanomachines operate in a highly fluctuating environment, which influences their operation. To bridge the gap between detailed microscopic and simple phenomenological descriptions, a mesoscale approach, which combines an elastic network model of a machine with a particle-based mesoscale description of the solvent, is employed. The time scale of the cyclic hinge motions of the machine prototype is strongly affected by hydrodynamical coupling to the solvent. Molecular machines, acting as motors, enzymes, or ion pumps, are involved in the function of biological cells ͓1,2͔ and are fundamental elements in applications in soft matter nanotechnology ͓3͔. Their operation relies on conformational motions in proteins, with time scales typically ranging between milliseconds and seconds. Although structures of machine proteins are known, full molecular dynamics simulations of such slow internal motions are currently not feasible. Therefore, theoretical analysis of stochastic dynamics of molecular machines is often based on simple models that describe these complex biomolecules as single-coordinate ratchets or oscillators ͓4,5͔. This approach has been successful in clarifying some principal aspects of machine operation ͓6͔, but the connection between simple phenomenological models and actual protein machines is often not evident. The gap between realistic microscopic models and simplified phenomenological approaches is an obstacle to the theoretical understanding of machine operation.Mesoscale models, which can fill this gap, are already broadly used to describe polymer dynamics and protein folding ͓7͔. In elastic network models, a protein is represented by a set of identical beads ͑coarse grained atomic groups͒ connected by identical elastic bonds; the pattern of connections is determined by the known equilibrium conformation of a given protein ͓8͔. Remarkably, elastic network models can reproduce large-amplitude slow conformational motions in many proteins ͓9͔, including some aspects of nonlinear conformational relaxation ͓10,11͔.Protein machines operate in molecular environments and solvent hydrodynamical effects play an important role in their dynamics. Solvent particles can be included in microscopic molecular dynamics simulations, but this is computationally challenging. In contrast, continuum hydrodynamical models cannot account for either molecular fluctuations or specific interactions. Coarse grained molecular dynamics schemes for the solvent can be used to incorporate both interactions and fluctuations ͓12͔.In this paper, we show that mesoscale models, combining coarse grained descriptions for molecular machines and their solvent environment, can provide an efficient intermediatelevel approach, bridging the gap between microscopic and macroscopic schemes. As an example, we consider an elastic network prototype of a molecu...

Background Previously incarcerated individuals have suboptimal linkage and engagement in community HIV care. Mobile health (mHealth) interventions have been shown to be effective in addressing these gaps. In Washington, District of Columbia (DC), we conducted a randomized trial of an SMS text messaging–based mHealth intervention (CARE+ Corrections) to increase linkage to community HIV care and antiretroviral treatment adherence among HIV-infected persons involved in the criminal justice system. Objective This study aimed to describe the SMS text messaging–based intervention, participant use of the intervention, and barriers and facilitators of implementation. Methods From August 2013 to April 2015, HIV-positive incarcerated individuals were recruited within the DC Department of Corrections, and persons released in the past 6 months were recruited within the community via street-based recruitment, community partnerships, and referrals. Participants were followed for 6 months and received weekly or daily SMS text messages. Formative research resulted in the development of the content of the messages in 4 categories: HIV Appointment Reminders, Medication Adherence, Prevention Reminders, and Barriers to Care following release from jail. Participants could customize the timing, frequency, and message content throughout the study period. Results Of the 112 participants enrolled, 57 (50.9%) were randomized to the intervention group and 55 (49.1%) to the control group; 2 control participants did not complete the baseline visit, and were dropped from the study, leaving a total of 110 participants who contributed to the analyses. Study retention was similar across both study arms. Median age was 42 years (IQR 30-50), 86% (49/57) were black or African American, 58% (33/57) were male, 25% (14/57) were female, and 18% (10/57) were transgender. Median length of last incarceration was 4 months (IQR 1.7-9.0), and median lifetime number of times incarcerated was 6.5 (IQR 3.5-14.0). Most participants (32/54, 59%) had a baseline viral load of <200 copies/mL. Nearly all participants (52/57, 91%) chose to use a cell phone provided by the study. The most preferred Appointment Reminder message was Hey how you feeling? Don’t forget to give a call and make your appointment (19/57, 33%). The most preferred Medication Adherence message was Don’t forget your skittles! (31/57, 54%), and 63% (36/57) of participants chose to receive daily (vs weekly) messages from this category at baseline. The most preferred Prevention Reminder message was Stay strong. Stay clean (18/57, 32%). The most preferred Barriers to Care message was Holla at your case manager, they’re here to help (12/57, 22%). Minor message preference differences were observed among participants enrolled in the jail versus those from the community. Conclusions Participants’ ability to customize their SMS text message plan proved helpful. Further large-scale research on mHealth platforms is needed to assess its efficacy among HIV-infected persons with a history of incarceration. Trial Registration ClinicalTrials.gov NCT01721226; https://clinicaltrials.gov/ct2/show/NCT01721226

contains supplementary material, which is available to authorized users. Conflict of interest The authors declare that they have no conflict of interest. Ethical Approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed Consent Informed consent was obtained from all individual participants included in the study. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Background HIV and syphilis infection continue at disproportionate rates among minority men who have sex with men (MSM) in the United States. The integration of HIV genetic clustering with partner services can provide important insight into local epidemic trends to guide interventions and control efforts. Methods We evaluated contact networks of index persons defined as minority men and transgender women diagnosed with early syphilis and/or HIV infection between 2018-2020 in two North Carolina regions. HIV clusters were constructed from pol sequences collected through statewide surveillance. A combined “HIV-risk" network, which included persons with any links (genetic or sexual contact) to HIV-positive persons, was evaluated by component size, demographic factors, and HIV viral suppression. Results In total, 1,289 index persons were identified and 55% named 1,153 contacts. Most index persons were Black (88%) and young (median age 30 years); 70% had early syphilis and 43% had prevalent HIV infection. Most people with HIV (65%) appeared in an HIV cluster. The combined HIV-risk network (1,590 contact network and 1,500 cluster members) included 287 distinct components; however, 1,586 (51%) were in a single component. Fifty-five percent of network members with HIV had no evidence of viral suppression. Overall, fewer index persons needed to be interviewed to identify one HIV-positive member without viral suppression (1.3 versus 4.0 for contact tracing). Conclusions Integration of HIV clusters and viral loads illuminate networks with high HIV prevalence, indicating recent and ongoing transmission. Interventions intensified towards these networks may efficiently reach persons for HIV prevention and care re-engagement.