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We have imaged lithium-6 thousands of times in an optical tweezer using Λ-enhanced gray molasses cooling light. Despite being the lightest alkali metal, with a recoil temperature of 3.5  μK, we achieve an imaging survival of 0.999 50(2), which sets the new benchmark for low-loss imaging of neutral atoms in optical tweezers. Lithium is loaded directly from a magneto-optical trap into a tweezer with an enhanced loading rate of 0.7. We cool the atom to 70  μK and present a new cooling model that accurately predicts steady-state temperature and scattering rate in the tweezer. These results pave the way for ground state preparation of lithium en route to the assembly of the LiCs molecule in its ground state.

Cryopreservation by vitrification to achieve an "ice free" glassy state is an effective technique for preserving biomaterials including cells, tissues, and potentially even whole organs. The major challenges in cooling to and rewarming from a vitrified state remain ice crystallization and cracking/fracture. Ice crystallization can be inhibited by the use of cryoprotective agents (CPAs), though the inhibition further depends upon the rates achieved during cooling and rewarming. The minimal rate required to prevent any ice crystallization or recrystallization/devitrification in a given CPA is called the critical cooling rate (CCR) or critical warming rate (CWR), respectively. On the other hand, physical cracking is mainly related to thermomechanical stresses, which can be avoided by maintaining temperature differences below a critical threshold. In this simplified analysis, we calculate deltaT as the largest temperature difference occurring in a system during cooling or rewarming in the brittle/glassy phase. This deltaT is then used in a simple "thermal shock equation" to estimate thermal stress within the material to decide if the material is above the yield strength and to evaluate the potential for fracture failure. In this review we aimed to understand the limits of success and failure at different length scales for cryopreservation by vitrification, due to both ice crystallization and cracking. Here we use thermal modeling to help us understand the magnitude and trajectory of these challenges as we scale the biomaterial volume for a given CPA from the milliliter to liter scale. First, we solved the governing heat transfer equations in a cylindrical geometry for three common vitrification cocktails (i.e., VS55, DP6, and M22) to estimate the cooling and warming rates during convective cooling and warming and nanowarming (volumetric heating). Second, we estimated the temperature difference deltaT and compared it to a tolerable threshold (deltaTmax) based on a simplified "thermal shock" equation for the same cooling and rewarming conditions. We found, not surprisingly, that M22 achieves vitrification more easily during convective cooling and rewarming for all volumes compared to VS55 or DP6 due to its considerably lower CCR and CWR. Further, convective rewarming (boundary rewarming) leads to larger temperature differences and smaller rates compared to nanowarming (volumetric rewarming) for all CPAs with increasing failure at larger volumes. We conclude that as more and larger systems are vitrified and rewarmed with standard CPA cocktails, this work can serve as a practical guide to successful implementation based on the characteristic length (volume/surface area) of the system and the specific conditions of cooling and warming. doi.org/10.54680/fr22610110112.

Background: Adaptive behavioral interventions are individualized interventions that vary support based on a person's evolving needs. Digital technologies enable these adaptive interventions to function at scale. Adaptive interventions show great promise for producing better results compared with static interventions related to health outcomes. Our central thesis is that adaptive interventions are more likely to succeed at helping individuals meet and maintain behavioral targets if its elements can be iteratively improved via data-driven testing (ie, optimization). Control systems engineering is a discipline focused on decision making in systems that change over time and has a wealth of methods that could be useful for optimizing adaptive interventions., Objective: The purpose of this paper was to provide an introductory tutorial on when and what to do when using control systems engineering for designing and optimizing adaptive mobile health (mHealth) behavioral interventions., Overview: We start with a review of the need for optimization, building on the multiphase optimization strategy (MOST). We then provide an overview of control systems engineering, followed by attributes of problems that are well matched to control engineering. Key steps in the development and optimization of an adaptive intervention from a control engineering perspective are then summarized, with a focus on why, what, and when to do subtasks in each step., Implications: Control engineering offers exciting opportunities for optimizing individualization and adaptation elements of adaptive interventions. Arguably, the time is now for control systems engineers and behavioral and health scientists to partner to advance interventions that can be individualized, adaptive, and scalable. This tutorial should aid in creating the bridge between these communities., (©Eric B Hekler, Daniel E Rivera, Cesar A Martin, Sayali S Phatak, Mohammad T Freigoun, Elizabeth Korinek, Predrag Klasnja, Marc A Adams, Matthew P Buman. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 28.06.2018.)

Background: Control systems engineering methods, particularly, system identification (system ID), offer an idiographic (i.e., person-specific) approach to develop dynamic models of physical activity (PA) that can be used to personalize interventions in a systematic, scalable way. The purpose of this work is to: (1) apply system ID to develop individual dynamical models of PA (steps/day measured using Fitbit Zip) in the context of a goal setting and positive reinforcement intervention informed by Social Cognitive Theory; and (2) compare insights on potential tailoring variables (i.e., predictors expected to influence steps and thus moderate the suggested step goal and points for goal achievement) selected using the idiographic models to those selected via a nomothetic (i.e., aggregated across individuals) approach., Method: A personalized goal setting and positive reinforcement intervention was deployed for 14 weeks. Baseline PA measured in weeks 1-2 was used to inform personalized daily step goals delivered in weeks 3-14. Goals and expected reward points (granted upon goal achievement) were pseudo-randomly assigned using techniques from system ID, with goals ranging from their baseline median steps/day up to 2.5× baseline median steps/day, and points ranging from 100 to 500 (i.e., $0.20-$1.00). Participants completed a series of daily self-report measures. Auto Regressive with eXogenous Input (ARX) modeling and multilevel modeling (MLM) were used as the idiographic and nomothetic approaches, respectively., Results: Participants (N = 20, mean age = 47.25 ± 6.16 years, 90% female) were insufficiently active, overweight (mean BMI = 33.79 ± 6.82 kg/m 2 ) adults. Results from ARX modeling suggest that individuals differ in the factors (e.g., perceived stress, weekday/weekend) that influence their observed steps/day. In contrast, the nomothetic model from MLM suggested that goals and weekday/weekend were the key variables that were predictive of steps. Assuming the ARX models are more personalized, the obtained nomothetic model would have led to the identification of the same predictors for 5 of the 20 participants, suggesting a mismatch of plausible tailoring variables to use for 75% of the sample., Conclusion: The idiographic approach revealed person-specific predictors beyond traditional MLM analyses and unpacked the inherent complexity of PA; namely that people are different and context matters. System ID provides a feasible approach to develop personalized dynamical models of PA and inform person-specific tailoring variable selection for use in adaptive behavioral interventions., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Adaptive interventions are an emerging class of behavioral interventions that allow for individualized tailoring of intervention components over time to a person's evolving needs. The purpose of this study was to evaluate an adaptive step goal + reward intervention, grounded in Social Cognitive Theory delivered via a smartphone application (Just Walk), using a mixed modeling approach. Participants (N = 20) were overweight (mean BMI = 33.8 ± 6.82 kg/m 2 ), sedentary adults (90% female) interested in participating in a 14-week walking intervention. All participants received a Fitbit Zip that automatically synced with Just Walk to track daily steps. Step goals and expected points were delivered through the app every morning and were designed using a pseudo-random multisine algorithm that was a function of each participant's median baseline steps. Self-report measures were also collected each morning and evening via daily surveys administered through the app. The linear mixed effects model showed that, on average, participants significantly increased their daily steps by 2650 (t = 8.25, p < 0.01) from baseline to intervention completion. A non-linear model with a quadratic time variable indicated an inflection point for increasing steps near the midpoint of the intervention and this effect was significant (t 2  = -247, t = -5.01, p < 0.001). An adaptive step goal + rewards intervention using a smartphone app appears to be a feasible approach for increasing walking behavior in overweight adults. App satisfaction was high and participants enjoyed receiving variable goals each day. Future mHealth studies should consider the use of adaptive step goals + rewards in conjunction with other intervention components for increasing physical activity.

Fungal infections are a global problem imposing considerable disease burden. One of the unmet needs in addressing these infections is rapid, sensitive diagnostics. A promising molecular diagnostic approach is high-resolution melt analysis (HRM). However, there has been little effort in leveraging HRM data for automated, objective identification of fungal species. The purpose of these studies was to assess the utility of distance methods developed for comparison of time series data to classify HRM curves as a means of fungal species identification. Dynamic time warping (DTW), first introduced in the context of speech recognition to identify temporal distortion of similar sounds, is an elastic distance measure that has been successfully applied to a wide range of time series data. Comparison of HRM curves of the rDNA internal transcribed spacer (ITS) region from 51 strains of 18 fungal species using DTW distances allowed accurate classification and clustering of all 51 strains. The utility of DTW distances for species identification was demonstrated by matching HRM curves from 243 previously identified clinical isolates against a database of curves from standard reference strains. The results revealed a number of prior misclassifications, discriminated species that are not resolved by routine phenotypic tests, and accurately identified all 243 test strains. In addition to DTW, several other distance functions, Edit Distance on Real sequence (EDR) and Shape-based Distance (SBD), showed promise. It is concluded that DTW-based distances provide a useful metric for the automated identification of fungi based on HRM curves of the ITS region and that this provides the foundation for a robust and automatable method applicable to the clinical setting.

In the Higgs mechanism, mediators of the weak force acquire masses by interacting with the Higgs condensate, leading to a vector boson mass matrix. On the other hand, a rigid body accelerated through an inviscid, incompressible and irrotational fluid feels an opposing force linearly related to its acceleration, via an added-mass tensor. We uncover a striking physical analogy between the two effects and propose a dictionary relating them. The correspondence turns the gauge Lie algebra into the space of directions in which the body can move, encodes the pattern of gauge symmetry breaking in the shape of an associated body and relates symmetries of the body to those of the scalar vacuum manifold. The new viewpoint is illustrated with numerous examples, and raises interesting questions, notably on the fluid analogues of the broken symmetry and Higgs particle, and the field-theoretic analogue of the added mass of a composite body.

Winning the relay: The first total synthesis of stemaphylline N-oxide has been completed utilizing a bistandem relay ring-closing-metathesis (RRCM) strategy, necessitated by the conformation of the requisite tetraene. This effort also gave unnatural 9a-epi-stemaphylline and 9a-epi-stemaphylline N-oxide., (Copyright © 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim.)

Exploiting drug polypharmacology to identify novel modes of actions for drug repurposing has gained significant attentions in the current era of weak drug pipelines. From a serendipitous to systematic or rational ways, a variety of unimodal computational approaches have been developed but the complexity of the problem clearly needs multi-modal approaches for better solutions. In this study, we propose an integrative computational framework based on classical structure-based drug design and chemical-genomic similarity methods, combined with molecular graph theories for this task. Briefly, a pharmacophore modeling method was employed to guide the selection of docked poses resulting from our high-throughput virtual screening. We then evaluated if complementary results (hits missed by docking) can be obtained by using a novel chemo-genomic similarity approach based on chemical/sequence information. Finally, we developed a bipartite-graph based on the extensive data curation of DrugBank, PDB, and UniProt. This drug-target bipartite graph was used to assess similarity of different inhibitors based on their connections to other compounds and targets. The approaches were applied to the repurposing of existing drugs against ACK1, a novel cancer target significantly overexpressed in breast and prostate cancers during their progression. Upon screening of ∼1,447 marketed drugs, a final set of 10 hits were selected for experimental testing. Among them, four drugs were identified as potent ACK1 inhibitors. Especially the inhibition of ACK1 by Dasatinib was as strong as IC(50)=1nM. We anticipate that our novel, integrative strategy can be easily extended to other biological targets with a more comprehensive coverage of known bio-chemical space for repurposing studies.

3-Formylchromone (3-FC) has been associated with anticancer potential through a mechanism yet to be elucidated. Because of the critical role of NF-κB in tumorigenesis, we investigated the effect of this agent on the NF-κB activation pathway. Whether activated by inflammatory agents (such as TNF-α and endotoxin) or tumor promoters (such as phorbol ester and okadaic acid), 3-FC suppressed NF-κB activation. It also inhibited constitutive NF-κB expressed by most tumor cells. This activity correlated with sequential inhibition of IκBα kinase (IKK) activation, IκBα phosphorylation, IκBα degradation, p65 phosphorylation, p65 nuclear translocation, and reporter gene expression. We found that 3-FC inhibited the direct binding of p65 to DNA, and this binding was reversed by a reducing agent, thus suggesting a role for the cysteine residue. Furthermore, mutation of Cys38 to Ser in p65 abolished this effect of the chromone. This result was confirmed by a docking study. 3-FC also inhibited IKK activation directly, and the reducing agent reversed this inhibition. Furthermore, mutation of Cys179 to Ala in IKK abolished the effect of the chromone. Suppression of NF-κB activation led to inhibition of anti-apoptotic (Bcl-2, Bcl-xL, survivin, and cIAP-1), proliferative (cyclin D1 and COX-2), invasive (MMP-9 and ICAM-1), and angiogenic (VEGF) gene products and sensitization of tumor cells to cytokines. Thus, this study shows that modification of cysteine residues in IKK and p65 by 3-FC leads to inhibition of the NF-κB activation pathway, suppression of anti-apoptotic gene products, and potentiation of apoptosis in tumor cells.

Boronic acids, known to bind diols, were screened to identify non-inflammatory cross-linkers for the preparation of glucose sensitive and insulin releasing agglomerates of liposomes (Agglomerated Vesicle Technology-AVT). This was done in order to select a suitable replacement for the previously used cross-linker, ConcanavalinA (ConA), a lectin known to have both toxic and inflammatory effects in vivo. Lead-compounds were selected from screens that involved testing for inflammatory potential, cytotoxicity and glucose-binding. These were then conjugated to insulin-encapsulating nanoparticles and agglomerated via sugar-boronate ester linkages to form AVTs. In vitro, the particles demonstrated triggered release of insulin upon exposure to physiologically relevant concentrations of glucose (10 mmoles/L-40 mmoles/L). The agglomerates were also shown to be responsive to multiple spikes in glucose levels over several hours, releasing insulin at a rate defined by the concentration of the glucose trigger.

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting proteinligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/ optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches.

G protein-coupled receptors (GPCRs) regulate a wide range of physiological functions and hold great pharmaceutical interest. Using the β(2)-adrenergic receptor as a case study, this article explores the applicability of docking-based virtual screening to the discovery of GPCR ligands and defines methods intended to improve the screening performance. Our controlled computational experiments were performed on a compound dataset containing known agonists and blockers of the receptor as well as a large number of decoys. The screening based on the structure of the receptor crystallized in complex with its inverse agonist carazolol yielded excellent results, with a clearly delineated prioritization of ligands over decoys. Blockers generally were preferred over agonists; however, agonists were also well distinguished from decoys. A method was devised to increase the screening yields by generating an ensemble of alternative conformations of the receptor that accounts for its flexibility. Moreover, a method was devised to improve the retrieval of agonists, based on the optimization of the receptor around a known agonist. Finally, the applicability of docking-based virtual screening also to homology models endowed with different levels of accuracy was proved. This last point is of uttermost importance, since crystal structures are available only for a limited number of GPCRs, and extends our conclusions to the entire superfamily. The outcome of this analysis definitely supports the application of computer-aided techniques to the discovery of novel GPCR ligands, especially in light of the fact that, in the near future, experimental structures are expected to be solved and become available for an ever increasing number of GPCRs., (Copyright © 2010 Elsevier Inc. All rights reserved.)

The drug discovery process mainly relies on the experimental high-throughput screening of huge compound libraries in their pursuit of new active compounds. However, spiraling research and development costs and unimpressive success rates have driven the development of more rational, efficient, and cost-effective methods. With the increasing availability of protein structural information, advancement in computational algorithms, and faster computing resources, in silico docking-based methods are increasingly used to design smaller and focused compound libraries in order to reduce screening efforts and costs and at the same time identify active compounds with a better chance of progressing through the optimization stages. This chapter is a primer on the various docking-based methods developed for the purpose of structure-based library design. Our aim is to elucidate some basic terms related to the docking technique and explain the methodology behind several docking-based library design methods. This chapter also aims to guide the novice computational practitioner by laying out the general steps involved for such an exercise. Selected successful case studies conclude this chapter.

Class A G-protein-coupled receptors (GPCRs) are among the most important targets for drug discovery. However, a large set of experimental structures, essential for a structure-based approach, will likely remain unavailable in the near future. Thus, there is an actual need for modeling tools to characterize satisfactorily at least the binding site of these receptors. Using experimentally solved GPCRs, we have enhanced and validated the ligand-steered homology method through cross-modeling and investigated the performance of the thus generated models in docking-based screening. The ligand-steered modeling method uses information about existing ligands to optimize the binding site by accounting for protein flexibility. We found that our method is able to generate quality models of GPCRs by using one structural template. These models perform better than templates, crude homology models, and random selection in small-scale high-throughput docking. Better quality models typically exhibit higher enrichment in docking exercises. Moreover, they were found to be reliable for selectivity prediction. Our results support the fact that the ligand-steered homology modeling method can successfully characterize pharmacologically relevant sites through a full flexible ligand-flexible receptor procedure.

We recently discovered and reported a series of N-alkyl-isatin acylhydrazone derivatives that are potent cannabinoid receptor 2 (CB(2)) agonists. In an effort to improve the druglike properties of these compounds and to better understand and improve the treatment of neuropathic pain, we designed and synthesized a new series of 2,3-dihydro-1-benzofuran derivatives bearing an asymmetric carbon atom that behave as potent selective CB(2) agonists. We used a multidisciplinary medicinal chemistry approach with binding mode prediction through ligand-steered modeling. Enantiomer separation and configuration assignment were carried out for the racemic mixture for the most selective compound, MDA7 (compound 18). It appeared that the S enantiomer, compound MDA104 (compound 33), was the active enantiomer. Compounds MDA42 (compound 19) and MDA39 (compound 30) were the most potent at CB(2). MDA42 was tested in a model of neuropathic pain and exhibited activity in the same range as that of MDA7. Preliminary ADMET studies for MDA7 were performed and did not reveal any problems.

Background: In the current situation of weak drug pipelines, impending patent expiration of several blockbuster drugs, industry consolidation and changing business models that target special diseases like cancer, diabetes, Alzheimer's and obesity, the pharmaceutical industry is under intense pressure to generate a strong drug pipeline distinguished by better productivity, diversity and cost effectiveness. The goal is discovering high-quality leads in the initial stages of the development cycle, to minimize the costs associated with failures at later ones., Objective: Thus, there is a great amount of interest in further developing and optimizing high-throughput screening and in silico screening, the two methods responsible for generating most of the lead compounds. Although high-throughput screening is the predominant starting point for discovery programs, in silico methods have gradually made inroads by their more rational approach, to expedite the drug discovery and development process., Conclusion: Modern drug discovery strategies include both methods in tandem or in an iterative way. This review primarily provides a succinct overview and comparison of experimental and in silico screening techniques, selected case studies where both methods were used in concert to investigate their performance and complementary nature and a statement on the developments in experimental and in silico approaches in the near future.

As structural genomics (SG) projects continue to deposit representative 3D structures of proteins, homology modeling methods will play an increasing role in structure-based drug discovery. Although computational structure prediction methods provide a cost-effective alternative in the absence of experimental structures, developing accurate enough models still remains a big challenge. In this contribution, we report the current developments in this field, discuss in silico modeling limitations, and review the successful application of this technique to different stages of the drug discovery process.

Recently, we discovered and reported a series of N-alkyl isatin acylhydrazone derivatives that are potent CB2 agonists. Here, we describe a novel series of selective CB2 inverse agonists resulting from introduction of a methoxy moiety in position 6 of the isatin scaffold. These novel 6-methoxy-N-alkyl isatin acylhydrazone derivatives exhibited high CB2 functional activity and selectivity at human CB2. Compound 16 (MDA77) had high activity (EC(50) = 5.82 nM) at CB2 and no activity at CB1. Compound 15 (MDA55) (K(i) = 89.9 nM, EC(50) = 88.2 nM at CB2) inhibited the effect of compound 1 (MDA7), a selective CB2 agonist, in an animal model of neuropathic pain. The molecular modeling study presented here represents a first study of CB2 based on the structure of beta(2)-adrenergic receptor. A ligand-based homology model of the CB2 binding site was developed, and on the basis of our results, we propose a general binding mode for this class of inverse agonists with CB2.