The parasite intraerythrocytic cycle and human circadian cycle are coupled during malaria infection.

During infections with the malaria parasites Plasmodium vivax, patients exhibit rhythmic fevers every 48 h. These fever cycles correspond with the time the parasites take to traverse the intraerythrocytic cycle (IEC). In other Plasmodium species that infect either humans or mice, the IEC is likely guided by a parasite-intrinsic clock [Rijo-Ferreiraet al., Science 368, 746-753 (2020); Smith et al., Science 368, 754-759 (2020)], suggesting that intrinsic clock mechanisms may be a fundamental feature of malaria parasites. Moreover, because Plasmodium cycle times are multiples of 24 h, the IECs may be coordinated with the host circadian clock(s). Such coordination could explain the synchronization of the parasite population in the host and enable alignment of IEC and circadian cycle phases. We utilized an ex vivo culture of whole blood from patients infected with P. vivax to examine the dynamics of the host circadian transcriptome and the parasite IEC transcriptome. Transcriptome dynamics revealed that the phases of the host circadian cycle and the parasite IEC are correlated across multiple patients, showing that the cycles are phase coupled. In mouse model systems, host-parasite cycle coupling appears to provide a selective advantage for the parasite. Thus, understanding how host and parasite cycles are coupled in humans could enable antimalarial therapies that disrupt this coupling.

Experimental guidance for discovering genetic networks through hypothesis reduction on time series.

Large programs of dynamic gene expression, like cell cyles and circadian rhythms, are controlled by a relatively small "core" network of transcription factors and post-translational modifiers, working in concerted mutual regulation. Recent work suggests that system-independent, quantitative features of the dynamics of gene expression can be used to identify core regulators. We introduce an approach of iterative network hypothesis reduction from time-series data in which increasingly complex features of the dynamic expression of individual, pairs, and entire collections of genes are used to infer functional network models that can produce the observed transcriptional program. The culmination of our work is a computational pipeline, Iterative Network Hypothesis Reduction from Temporal Dynamics (Inherent dynamics pipeline), that provides a priority listing of targets for genetic perturbation to experimentally infer network structure. We demonstrate the capability of this integrated computational pipeline on synthetic and yeast cell-cycle data.

Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes.

BACKGROUND: Cell and circadian cycles control a large fraction of cell and organismal physiology by regulating large periodic transcriptional programs that encompass anywhere from 15 to 80% of the genome despite performing distinct functions. In each case, these large periodic transcriptional programs are controlled by gene regulatory networks (GRNs), and it has been shown through genetics and chromosome mapping approaches in model systems that at the core of these GRNs are small sets of genes that drive the transcript dynamics of the GRNs. However, it is unlikely that we have identified all of these core genes, even in model organisms. Moreover, large periodic transcriptional programs controlling a variety of processes certainly exist in important non-model organisms where genetic approaches to identifying networks are expensive, time-consuming, or intractable. Ideally, the core network components could be identified using data-driven approaches on the transcriptome dynamics data already available. RESULTS: This study shows that a unified set of quantified dynamic features of high-throughput time series gene expression data are more prominent in the core transcriptional regulators of cell and circadian cycles than in their outputs, in multiple organism, even in the presence of external periodic stimuli. Additionally, we observe that the power to discriminate between core and non-core genes is largely insensitive to the particular choice of quantification of these features. CONCLUSIONS: There are practical applications of the approach presented in this study for network inference, since the result is a ranking of genes that is enriched for core regulatory elements driving a periodic phenotype. In this way, the method provides a prioritization of follow-up genetic experiments. Furthermore, these findings reveal something unexpected-that there are shared dynamic features of the transcript abundance of core components of unrelated GRNs that control disparate periodic phenotypes.

Improved datasets and evaluation methods for the automatic prediction of DNA-binding proteins.

MOTIVATION: Accurate automatic annotation of protein function relies on both innovative models and robust datasets. Due to their importance in biological processes, the identification of DNA-binding proteins directly from protein sequence has been the focus of many studies. However, the datasets used to train and evaluate these methods have suffered from substantial flaws. We describe some of the weaknesses of the datasets used in previous DNA-binding protein literature and provide several new datasets addressing these problems. We suggest new evaluative benchmark tasks that more realistically assess real-world performance for protein annotation models. We propose a simple new model for the prediction of DNA-binding proteins and compare its performance on the improved datasets to two previously published models. In addition, we provide extensive tests showing how the best models predict across taxa. RESULTS: Our new gradient boosting model, which uses features derived from a published protein language model, outperforms the earlier models. Perhaps surprisingly, so does a baseline nearest neighbor model using BLAST percent identity. We evaluate the sensitivity of these models to perturbations of DNA-binding regions and control regions of protein sequences. The successful data-driven models learn to focus on DNA-binding regions. When predicting across taxa, the best models are highly accurate across species in the same kingdom and can provide some information when predicting across kingdoms. AVAILABILITY AND IMPLEMENTATION: The data and results for this article can be found at https://doi.org/10.5281/zenodo.5153906. The code for this article can be found at https://doi.org/10.5281/zenodo.5153683. The code, data and results can also be found at https://github.com/AZaitzeff/tools_for_dna_binding_proteins.

Trust and Distrust in Opioid Decision-Making: A Qualitative Assessment of Patient-Doctor Relationship.

PURPOSE: Surgeons often prescribe opioid analgesics for pain management after surgery. However, we understand little about how patients perceive opioid prescribing and make decisions to use opioids for postoperative pain management. In this study, we aimed to gain an understanding of patients' decision-making process on postoperative opioid use. METHODS: We conducted semi-structured interviews with 30 adult patients undergoing elective surgery at our institution. The interviews were content-coded for thematic analysis. We used trust in the medical setting as a conceptual framework to interpret and find the inherent theory in the data. RESULTS: We found that participants based their opioid decisions on their trust or distrust toward various elements of their postoperative pain management. Participants believed that the surgeons "know," thereby, reinforcing their trust in surgeons' postoperative opioid prescribing to be in the participants' best interest. Moreover, the positive reputation of the institution strengthened the participants' trust. However, participants conveyed nuanced trust because of their distrust toward the opioid medications themselves, which were viewed as "suspicious," and the pharmaceutical companies, that were "despised." Despite this distrust, participants had confidence in their inherent ability to protect themselves from opioid use disorders. CONCLUSIONS: Understanding how patients perceive and form decisions on postoperative opioid use based on their trust and distrust toward various factors involved in their care highlights the importance of the patient-doctor relationship and building trust to effectively address postoperative pain and reduce opioid-related harms. CLINICAL RELEVANCE: Through a strengthened therapeutic alliance between patients and surgeons, we can improve our strategies to overcome the ongoing opioid epidemic through patient-centered approaches.

Implementation of a Pooled Surveillance Testing Program for Asymptomatic SARS-CoV-2 Infections on a College Campus - Duke University, Durham, North Carolina, August 2-October 11, 2020.

On university campuses and in similar congregate environments, surveillance testing of asymptomatic persons is a critical strategy (1,2) for preventing transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). All students at Duke University, a private research university in Durham, North Carolina, signed the Duke Compact (3), agreeing to observe mandatory masking, social distancing, and participation in entry and surveillance testing. The university implemented a five-to-one pooled testing program for SARS-CoV-2 using a quantitative, in-house, laboratory-developed, real-time reverse transcription-polymerase chain reaction (RT-PCR) test (4,5). Pooling of specimens to enable large-scale testing while minimizing use of reagents was pioneered during the human immunodeficiency virus pandemic (6). A similar methodology was adapted for Duke University's asymptomatic testing program. The baseline SARS-CoV-2 testing plan was to distribute tests geospatially and temporally across on- and off-campus student populations. By September 20, 2020, asymptomatic testing was scaled up to testing targets, which include testing for residential undergraduates twice weekly, off-campus undergraduates one to two times per week, and graduate students approximately once weekly. In addition, in response to newly identified positive test results, testing was focused in locations or within cohorts where data suggested an increased risk for transmission. Scale-up over 4 weeks entailed redeploying staff members to prepare 15 campus testing sites for specimen collection, developing information management tools, and repurposing laboratory automation to establish an asymptomatic surveillance system. During August 2-October 11, 68,913 specimens from 10,265 graduate and undergraduate students were tested. Eighty-four specimens were positive for SARS-CoV-2, and 51% were among persons with no symptoms. Testing as a result of contact tracing identified 27.4% of infections. A combination of risk-reduction strategies and frequent surveillance testing likely contributed to a prolonged period of low transmission on campus. These findings highlight the importance of combined testing and contact tracing strategies beyond symptomatic testing, in association with other preventive measures. Pooled testing balances resource availability with supply-chain disruptions, high throughput with high sensitivity, and rapid turnaround with an acceptable workload.

Binary vs. continuous experimental designs for the study of unconscious perceptual processing.

Binary vs. continuous conceptualizations of consciousness may have an unstated influence on experimental designs in unconscious perception research. The binary approach aims to compare a conscious condition (e.g., supraliminal, no or weak stimulus masking) to an unconscious condition (e.g., subliminal, heavy stimulus masking). In contrast, continuous designs tend to vary stimulus energy along a near-threshold continuum to determine changes in perception as a function of stimulus energy (or duration). The present study compared two experimental designs, binary vs. continuous, for the influence of target-masked prime stimuli on a Stroop task. The display parameters were inspired by emotional Stroop studies reporting unconscious perception. Neither experiment produced strong evidence of unconscious perception, but the experiment with a continuous design was more informative. We thus recommend sampling a range of near-threshold display parameters to yield straight-forward, unambiguous interpretations.

Cyclin-dependent kinases are regulators and effectors of oscillations driven by a transcription factor network.

During embryonic cell cycles, B-cyclin-CDKs function as the core component of an autonomous oscillator. Current models for the cell-cycle oscillator in nonembryonic cells are slightly more complex, incorporating multiple G1, S phase, and mitotic cyclin-CDK complexes. However, periodic events persist in yeast cells lacking all S phase and mitotic B-cyclin genes, challenging the assertion that cyclin-CDK complexes are essential for oscillations. These and other results led to the proposal that a network of sequentially activated transcription factors functions as an underlying cell-cycle oscillator. Here we examine the individual contributions of a transcription factor network and cyclin-CDKs to the maintenance of cell-cycle oscillations. Our findings suggest that while cyclin-CDKs are not required for oscillations, they do contribute to oscillation robustness. A model emerges in which cyclin expression (thereby, CDK activity) is entrained to an autonomous transcriptional oscillator. CDKs then modulate oscillator function and serve as effectors of the oscillator.

Using extremal events to characterize noisy time series.

Experimental time series provide an informative window into the underlying dynamical system, and the timing of the extrema of a time series (or its derivative) contains information about its structure. However, the time series often contain significant measurement errors. We describe a method for characterizing a time series for any assumed level of measurement error [Formula: see text] by a sequence of intervals, each of which is guaranteed to contain an extremum for any function that [Formula: see text]-approximates the time series. Based on the merge tree of a continuous function, we define a new object called the normalized branch decomposition, which allows us to compute intervals for any level [Formula: see text]. We show that there is a well-defined total order on these intervals for a single time series, and that it is naturally extended to a partial order across a collection of time series comprising a dataset. We use the order of the extracted intervals in two applications. First, the partial order describing a single dataset can be used to pattern match against switching model output (Cummins et al. in SIAM J Appl Dyn Syst 17(2):1589-1616, 2018), which allows the rejection of a network model. Second, the comparison between graph distances of the partial orders of different datasets can be used to quantify similarity between biological replicates.

Investigating Conservation of the Cell-Cycle-Regulated Transcriptional Program in the Fungal Pathogen, Cryptococcus neoformans.

The pathogenic yeast Cryptococcus neoformans causes fungal meningitis in immune-compromised patients. Cell proliferation in the budding yeast form is required for C. neoformans to infect human hosts, and virulence factors such as capsule formation and melanin production are affected by cell-cycle perturbation. Thus, understanding cell-cycle regulation is critical for a full understanding of virulence factors for disease. Our group and others have demonstrated that a large fraction of genes in Saccharomyces cerevisiae is expressed periodically during the cell cycle, and that proper regulation of this transcriptional program is important for proper cell division. Despite the evolutionary divergence of the two budding yeasts, we found that a similar percentage of all genes (~20%) is periodically expressed during the cell cycle in both yeasts. However, the temporal ordering of periodic expression has diverged for some orthologous cell-cycle genes, especially those related to bud emergence and bud growth. Genes regulating DNA replication and mitosis exhibited a conserved ordering in both yeasts, suggesting that essential cell-cycle processes are conserved in periodicity and in timing of expression (i.e. duplication before division). In S. cerevisiae cells, we have proposed that an interconnected network of periodic transcription factors (TFs) controls the bulk of the cell-cycle transcriptional program. We found that temporal ordering of orthologous network TFs was not always maintained; however, the TF network topology at cell-cycle commitment appears to be conserved in C. neoformans. During the C. neoformans cell cycle, DNA replication genes, mitosis genes, and 40 genes involved in virulence are periodically expressed. Future work toward understanding the gene regulatory network that controls cell-cycle genes is critical for developing novel antifungals to inhibit pathogen proliferation.

The Impact of Pre-Referral Advanced Diagnostic Testing on Wait Time to See a Hand Surgeon for Common Upper-Extremity Conditions.

PURPOSE: We sought to evaluate the use of pre- and post-referral advanced diagnostic testing among patients with 3 common hand conditions, rates of subsequent tests, and differences in wait time to see a hand surgeon. METHODS: We analyzed a single academic tertiary care center administrative database of encounters from 2006 to 2015 to identify adult patients who were referred to a hand surgeon for 3 conditions (carpal tunnel syndrome [CTS], soft tissue masses [STM], and joint pain [JP]). We recorded patient characteristics, use and timing of diagnostic tests, and wait time for the initial hand surgeon evaluation. RESULTS: Among patients who received advanced diagnostic tests before the surgeon evaluation, CTS patients had the highest rate of receiving pre-referral advanced testing (53.4%) compared with JP (10.6% ) and STM patients (5.8%). The CTS patients had the highest rates of repeat testing (19.5%) compared with patients with JP (1.4%) and STM (0%). Across all 3 conditions, patients who received pre-referral advanced testing waited an additional 19 to 94 days to see a surgeon, compared with patients who received only post-referral testing or no testing. CONCLUSIONS: Use of pre-referral advanced diagnostic tests is associated with an increased time to see a hand surgeon for common hand conditions. CLINICAL RELEVANCE: Hand surgeons should have a role in identifying patients who do or do not benefit from advanced testing before referral to ensure that tests ordered before consultation are useful to both patients and treating surgeons.

Connecting virulence pathways to cell-cycle progression in the fungal pathogen Cryptococcus neoformans.

Proliferation and host evasion are critical processes to understand at a basic biological level for improving infectious disease treatment options. The human fungal pathogen Cryptococcus neoformans causes fungal meningitis in immunocompromised individuals by proliferating in cerebrospinal fluid. Current antifungal drugs target "virulence factors" for disease, such as components of the cell wall and polysaccharide capsule in C. neoformans. However, mechanistic links between virulence pathways and the cell cycle are not as well studied. Recently, cell-cycle synchronized C. neoformans cells were profiled over time to identify gene expression dynamics (Kelliher et al., PLoS Genet 12(12):e1006453, 2016). Almost 20% of all genes in the C. neoformans genome were periodically activated during the cell cycle in rich media, including 40 genes that have previously been implicated in virulence pathways. Here, we review important findings about cell-cycle-regulated genes in C. neoformans and provide two examples of virulence pathways-chitin synthesis and G-protein coupled receptor signaling-with their putative connections to cell division. We propose that a "comparative functional genomics" approach, leveraging gene expression timing during the cell cycle, orthology to genes in other fungal species, and previous experimental findings, can lead to mechanistic hypotheses connecting the cell cycle to fungal virulence.

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program.

Due to cell-to-cell variability and asymmetric cell division, cells in a synchronized population lose synchrony over time. As a result, time-series measurements from synchronized cell populations do not reflect the underlying dynamics of cell-cycle processes. Here, we present a branching process deconvolution algorithm that learns a more accurate view of dynamic cell-cycle processes, free from the convolution effects associated with imperfect cell synchronization. Through wavelet-basis regularization, our method sharpens signal without sharpening noise and can remarkably increase both the dynamic range and the temporal resolution of time-series data. Although applicable to any such data, we demonstrate the utility of our method by applying it to a recent cell-cycle transcription time course in the eukaryote Saccharomyces cerevisiae. Our method more sensitively detects cell-cycle-regulated transcription and reveals subtle timing differences that are masked in the original population measurements. Our algorithm also explicitly learns distinct transcription programs for mother and daughter cells, enabling us to identify 82 genes transcribed almost entirely in early G1 in a daughter-specific manner.

Awareness of "Invisible" Arrows in a Metacontrast Masking Paradigm.

A common strategy in unconscious perception research is to use either pattern masking or metacontrast masking to render prime stimuli "invisible" to consciousness. However, several recent studies have questioned whether the identities of prime stimuli (typically arrows or diamonds and squares) in metacontrast masking studies are impossible to consciously perceive. In a series of studies, we concurrently related prime awareness, target response time priming, and prime identification across 3 prime-mask stimulus onset asynchronies (27, 40, and 67 ms). We found that increases in prime awareness ratings were accompanied by better prime identification performance. Significant prime identification in the 27-ms condition was obtained only at the highest awareness rating; for the other 2 stimulus onset asynchronies most awareness ratings were associated with above-chance prime identification. The priming effects obtained in these paradigms occur, to some degree, when participants are likely to be aware of the prime stimuli. Our results, collectively, suggest that metacontrast masking of primes does not necessarily preclude their awareness. Priming effects may depend on at least partial awareness of the prime stimuli.

Design and analysis of large-scale biological rhythm studies: a comparison of algorithms for detecting periodic signals in biological data.

MOTIVATION: To discover and study periodic processes in biological systems, we sought to identify periodic patterns in their gene expression data. We surveyed a large number of available methods for identifying periodicity in time series data and chose representatives of different mathematical perspectives that performed well on both synthetic data and biological data. Synthetic data were used to evaluate how each algorithm responds to different curve shapes, periods, phase shifts, noise levels and sampling rates. The biological datasets we tested represent a variety of periodic processes from different organisms, including the cell cycle and metabolic cycle in Saccharomyces cerevisiae, circadian rhythms in Mus musculus and the root clock in Arabidopsis thaliana. RESULTS: From these results, we discovered that each algorithm had different strengths. Based on our findings, we make recommendations for selecting and applying these methods depending on the nature of the data and the periodic patterns of interest. Additionally, these results can also be used to inform the design of large-scale biological rhythm experiments so that the resulting data can be used with these algorithms to detect periodic signals more effectively.

Global control of cell-cycle transcription by coupled CDK and network oscillators.

A significant fraction of the Saccharomyces cerevisiae genome is transcribed periodically during the cell division cycle, indicating that properly timed gene expression is important for regulating cell-cycle events. Genomic analyses of the localization and expression dynamics of transcription factors suggest that a network of sequentially expressed transcription factors could control the temporal programme of transcription during the cell cycle. However, directed studies interrogating small numbers of genes indicate that their periodic transcription is governed by the activity of cyclin-dependent kinases (CDKs). To determine the extent to which the global cell-cycle transcription programme is controlled by cyclin-CDK complexes, we examined genome-wide transcription dynamics in budding yeast mutant cells that do not express S-phase and mitotic cyclins. Here we show that a significant fraction of periodic genes are aberrantly expressed in the cyclin mutant. Although cells lacking cyclins are blocked at the G1/S border, nearly 70% of periodic genes continued to be expressed periodically and on schedule. Our findings reveal that although CDKs have a function in the regulation of cell-cycle transcription, they are not solely responsible for establishing the global periodic transcription programme. We propose that periodic transcription is an emergent property of a transcription factor network that can function as a cell-cycle oscillator independently of, and in tandem with, the CDK oscillator.

Predictors of functional outcomes after simple decompression for ulnar neuropathy at the elbow: a multicenter study by the SUN study group.

OBJECTIVE: To identify predictors of surgical outcome for ulnar neuropathy at the elbow (UNE). DESIGN: Prospective cohort followed for 1 year. SETTING: Clinics. PARTICIPANTS: Patients diagnosed with UNE (N=55). INTERVENTION: All subjects had simple decompression surgery. MAIN OUTCOME MEASURES: The primary outcome measure was patient-reported outcomes, such as overall hand function through the Michigan Hand Outcomes Questionnaire (MHQ). Predictors included age, duration of symptoms, disease severity, and motor conduction velocity across the elbow. RESULTS: Multiple regression models with change in the overall MHQ score as the dependent variable showed that at 3 months postoperative time, patients with <3 months duration of symptoms showed 12 points (95% confidence interval [CI], 0.9-23.5) greater improvement in MHQ scores than those with ≥3 months symptom duration. Less than 3 months of symptoms was again associated with 13 points (95% CI, 2.9-24) greater improvement in MHQ scores at 6 months postoperative, but it was no longer associated with better outcomes at 12 months. A worse baseline MHQ score was associated with significant improvement in MHQ scores at 3 months (coefficient, -0.38; 95% CI, -.67 to -.09), and baseline MHQ score was the only significant predictor of 12 month MHQ scores (coefficient, -.40; 95% CI, -.79 to -.01). CONCLUSIONS: Subjects with <3 months of symptoms and worse baseline MHQ scores showed significantly greater improvement in functional outcomes as reported by the MHQ. However, duration of symptoms was only predictive at 3 or 6 months because most patients recovered within 3 to 6 months after surgery.

A yeast cell cycle pulse generator model shows consistency with multiple oscillatory and checkpoint mutant datasets.

Modeling biological systems holds great promise for speeding up the rate of discovery in systems biology by predicting experimental outcomes and suggesting targeted interventions. However, this process is dogged by an identifiability issue, in which network models and their parameters are not sufficiently constrained by coarse and noisy data to ensure unique solutions. In this work, we evaluated the capability of a simplified yeast cell-cycle network model to reproduce multiple observed transcriptomic behaviors under genomic mutations. We matched time-series data from both cycling and checkpoint arrested cells to model predictions using an asynchronous multi-level Boolean approach. We showed that this single network model, despite its simplicity, is capable of exhibiting dynamical behavior similar to the datasets in most cases, and we demonstrated the drop in severity of the identifiability issue that results from matching multiple datasets.

Highly correlated stimuli do not necessarily facilitate the measurement of unconscious perception: exclusion failure is hard to find in forced-choice tasks.

Persaud and McLeod (2008) report that unconscious perception is easier to measure with forced-choice exclusion tasks when the stimuli are highly similar, such as choosing between the letters 'h' and 'b'. The high degree of stimulus similarity may decrease conscious awareness of the target stimuli while leaving unconscious cognition intact. The present experiments used forced-choice exclusion tasks (i.e., choosing the opposite of a masked target stimulus) with the aim of replicating these findings. No evidence of relevant perception - either conscious or unconscious - was obtained with short duration targets. The forced-choice exclusion task was correctly performed at longer target durations (25 ms and higher), which suggests conscious perception of the target stimuli. We conclude that increasing stimulus similarity does not reliably produce exclusion failure effects and does not appear to facilitate the measurement of unconscious cognition.

B-cyclin/CDKs regulate mitotic spindle assembly by phosphorylating kinesins-5 in budding yeast.

Although it has been known for many years that B-cyclin/CDK complexes regulate the assembly of the mitotic spindle and entry into mitosis, the full complement of relevant CDK targets has not been identified. It has previously been shown in a variety of model systems that B-type cyclin/CDK complexes, kinesin-5 motors, and the SCF(Cdc4) ubiquitin ligase are required for the separation of spindle poles and assembly of a bipolar spindle. It has been suggested that, in budding yeast, B-type cyclin/CDK (Clb/Cdc28) complexes promote spindle pole separation by inhibiting the degradation of the kinesins-5 Kip1 and Cin8 by the anaphase-promoting complex (APC(Cdh1)). We have determined, however, that the Kip1 and Cin8 proteins are present at wild-type levels in the absence of Clb/Cdc28 kinase activity. Here, we show that Kip1 and Cin8 are in vitro targets of Clb2/Cdc28 and that the mutation of conserved CDK phosphorylation sites on Kip1 inhibits spindle pole separation without affecting the protein's in vivo localization or abundance. Mass spectrometry analysis confirms that two CDK sites in the tail domain of Kip1 are phosphorylated in vivo. In addition, we have determined that Sic1, a Clb/Cdc28-specific inhibitor, is the SCF(Cdc4) target that inhibits spindle pole separation in cells lacking functional Cdc4. Based on these findings, we propose that Clb/Cdc28 drives spindle pole separation by direct phosphorylation of kinesin-5 motors.