Network Spreading from Network Dimension.

Continuous-state network spreading models provide critical numerical and analytic insights into transmission processes in epidemiology, rumor propagation, knowledge dissemination, and many other areas. Most of these models reflect only local features such as adjacency, degree, and transitivity, so can exhibit substantial error in the presence of global correlations typical of empirical networks. Here, we propose mitigating this limitation via a network property ideally suited to capturing spreading. This is the network correlation dimension, which characterizes how the number of nodes within range of a source typically scales with distance. Applying the approach to susceptible-infected-recovered processes leads to a spreading model which, for a wide range of networks and epidemic parameters, can provide more accurate predictions of the early stages of a spreading process than important established models of substantially higher complexity. In addition, the proposed model leads to a basic reproduction number that provides information about the final state not available from popular established models.

Epidemic dynamics on higher-dimensional small world networks.

Dimension governs dynamical processes on networks. The social and technological networks which we encounter in everyday life span a wide range of dimensions, but studies of spreading on finite-dimensional networks are usually restricted to one or two dimensions. To facilitate investigation of the impact of dimension on spreading processes, we define a flexible higher-dimensional small world network model and characterize the dependence of its structural properties on dimension. Subsequently, we derive mean field, pair approximation, intertwined continuous Markov chain and probabilistic discrete Markov chain models of a COVID-19-inspired susceptible-exposed-infected-removed (SEIR) epidemic process with quarantine and isolation strategies, and for each model identify the basic reproduction number R 0 , which determines whether an introduced infinitesimal level of infection in an initially susceptible population will shrink or grow. We apply these four continuous state models, together with discrete state Monte Carlo simulations, to analyse how spreading varies with model parameters. Both network properties and the outcome of Monte Carlo simulations vary substantially with dimension or rewiring rate, but predictions of continuous state models change only slightly. A different trend appears for epidemic model parameters: as these vary, the outcomes of Monte Carlo change less than those of continuous state methods. Furthermore, under a wide range of conditions, the four continuous state approximations present similar deviations from the outcome of Monte Carlo simulations. This bias is usually least when using the pair approximation model, varies only slightly with network size, and decreases with dimension or rewiring rate. Finally, we characterize the discrepancies between Monte Carlo and continuous state models by simultaneously considering network efficiency and network size.

Kidney retransplantation after anti-programmed cell death-1 (PD-1)-related allograft rejection.

In this report, we describe the first kidney retransplantation performed after anti-programmed cell death-1 (PD-1)-related allograft rejection. In 2014, we administered pembrolizumab (anti-PD-1) for ~9 months to a 57-year-old kidney transplant recipient with metastatic cutaneous squamous cell carcinoma (CSCC). The patient experienced both a complete antitumor response and T cell-mediated allograft rejection requiring reinitiation of hemodialysis. Four-and-a-half years after initiating pembrolizumab, the patient remained without evidence of CSCC relapse and received a kidney transplant from a living-unrelated donor. Ten-and-a-half months after kidney retransplantation, the allograft is functioning well and the patient's CSCC remains in remission. This case illustrates the potential for PD-1 blockade to bring about durable immune-mediated tumor control in chronically immunosuppressed patients, and begins to address the feasibility of kidney retransplantation in patients who have previously received immune checkpoint inhibitor therapy for cancer. Results from this and future cases may help elucidate mechanisms of antitumor immunity and allograft tolerance, and inform updates to transplant decision models. Our report also underscores the need for clinical trials testing novel immunotherapy combinations in solid organ transplant recipients designed to uncouple antitumor and anti-allograft immunity.

Growing networks with communities: A distributive link model.

Evolution and popularity are two keys of the Barabasi-Albert model, which generates a power law distribution of network degrees. Evolving network generation models are important as they offer an explanation of both how and why complex networks (and scale-free networks, in particular) are ubiquitous. We adopt the evolution principle and then propose a very simple and intuitive new model for network growth, which naturally evolves modular networks with multiple communities. The number and size of the communities evolve over time and are primarily subjected to a single free parameter. Surprisingly, under some circumstances, our framework can construct a tree-like network with clear community structures-branches and leaves of a tree. Results also show that new communities will absorb a link resource to weaken the degree growth of hub nodes. Our models have a common explanation for the community of regular and tree-like networks and also breaks the tyranny of the early adopter; unlike the standard popularity principle, newer nodes and communities will come to dominance over time. Importantly, our model can fit well with the construction of the SARS-Cov-2 haplotype evolutionary network.

The sole LSm complex in Cyanidioschyzon merolae associates with pre-mRNA splicing and mRNA degradation factors.

Proteins of the Sm and Sm-like (LSm) families, referred to collectively as (L)Sm proteins, are found in all three domains of life and are known to promote a variety of RNA processes such as base-pair formation, unwinding, RNA degradation, and RNA stabilization. In eukaryotes, (L)Sm proteins have been studied, inter alia, for their role in pre-mRNA splicing. In many organisms, the LSm proteins form two distinct complexes, one consisting of LSm1-7 that is involved in mRNA degradation in the cytoplasm, and the other consisting of LSm2-8 that binds spliceosomal U6 snRNA in the nucleus. We recently characterized the splicing proteins from the red alga Cyanidioschyzon merolae and found that it has only seven LSm proteins. The identities of CmLSm2-CmLSm7 were unambiguous, but the seventh protein was similar to LSm1 and LSm8. Here, we use in vitro binding measurements, microscopy, and affinity purification-mass spectrometry to demonstrate a canonical splicing function for the C. merolae LSm complex and experimentally validate our bioinformatic predictions of a reduced spliceosome in this organism. Copurification of Pat1 and its associated mRNA degradation proteins with the LSm proteins, along with evidence of a cytoplasmic fraction of CmLSm complexes, argues that this complex is involved in both splicing and cytoplasmic mRNA degradation. Intriguingly, the Pat1 complex also copurifies with all four snRNAs, suggesting the possibility of a spliceosome-associated pre-mRNA degradation complex in the nucleus.

Identification and Characterization of Novel Receptor-Interacting Serine/Threonine-Protein Kinase 2 Inhibitors Using Structural Similarity Analysis.

Receptor-interacting protein kinase 2 (RIP2 or RICK, herein referred to as RIPK2) is linked to the pathogen pathway that activates nuclear factor κ-light-chain-enhancer of activated B cells (NFκB) and autophagic activation. Using molecular modeling (docking) and chemoinformatics analyses, we used the RIPK2/ponatinib crystal structure and searched in chemical databases for small molecules exerting binding interactions similar to those exerted by ponatinib. The identified RIPK2 inhibitors potently inhibited the proliferation of cancer cells by > 70% and also inhibited NFκB activity. More importantly, in vivo inhibition of intestinal and lung inflammation rodent models suggests effectiveness to resolve inflammation with low toxicity to the animals. Thus, our identified RIPK2 inhibitor may offer possible therapeutic control of inflammation in diseases such as inflammatory bowel disease, asthma, cystic fibrosis, primary sclerosing cholangitis, and pancreatitis.

Is Bach's brain a Markov chain? Recurrence quantification to assess Markov order for short, symbolic, musical compositions.

It is rarely possible to precisely characterise the system underlying a series of observations. Hypothesis testing, which involves assessing simple assumptions about driving mechanisms, provides hope that we can at least rule out certain possibilities regarding the nature of the system. Unfortunately, the brevity, nonstationarity, and symbolic nature of certain time series of interest undermines traditional hypothesis tests. Fortunately, recurrence quantification analysis (RQA) has an established record of success in understanding short and nonstationary time series. We evaluate the suitability of measures of RQA as test statistics in surrogate data tests of the hypothesis that ten compositions by the Baroque composer J. S. Bach (1685-1750) arose from a Markov chain. More specifically, we estimate the size (the rate at which true hypotheses are incorrectly rejected) and power (the rate at which false hypotheses are correctly rejected) from empirical rejection rates across 1000 realisations, for each of the ten compositions, of the surrogate algorithm. We compare hypothesis tests based on RQA measures to tests based on the conditional entropy, an established test statistic for surrogate data tests of Markov order, and find that the RQA measure Lmax provides more consistent rejection of the fairly implausible hypothesis that Bach's brain was a Markov chain.

Improvements to local projective noise reduction through higher order and multiscale refinements.

The broad spectrum characteristic of signals from nonlinear systems obstructs noise reduction techniques developed for linear systems. Local projection was developed to reduce noise while preserving nonlinear deterministic structures, and a second order refinement to local projection which was proposed ten years ago does so particularly effectively. It involves adjusting the origin of the projection subspace to better accommodate the geometry of the attractor. This paper describes an analytic motivation for the enhancement from which follows further higher order and multiple scale refinements. However, the established enhancement is frequently as or more effective than the new filters arising from solely geometric considerations. Investigation of the way that measurement errors reinforce or cancel throughout the refined local projection procedure explains the special efficacy of the existing enhancement, and leads to a new second order refinement offering widespread gains. Different local projective filters are found to be best suited to different noise levels. At low noise levels, the optimal order increases as noise increases. At intermediate levels second order tends to be optimal, while at high noise levels prototypical local projection is most effective. The new higher order filters perform better relative to established filters for longer signals or signals corresponding to higher dimensional attractors.

Correlation dimension in empirical networks.

Network correlation dimension governs the distribution of network distance in terms of a power-law model and profoundly impacts both structural properties and dynamical processes. We develop new maximum likelihood methods which allow us robustly and objectively to identify network correlation dimension and a bounded interval of distances over which the model faithfully represents structure. We also compare the traditional practice of estimating correlation dimension by modeling as a power law the fraction of nodes within a distance to a proposed alternative of modeling as a power law the fraction of nodes at a distance. In addition, we illustrate a likelihood ratio technique for comparing the correlation dimension and small-world descriptions of network structure. Improvements from our innovations are demonstrated on a diverse selection of synthetic and empirical networks. We show that the network correlation dimension model accurately captures empirical network structure over neighborhoods of substantial size and span and outperforms the alternative small-world network scaling model. Our improved methods tend to lead to higher estimates of network correlation dimension, implying that prior studies could have produced or utilized systematic underestimates of dimension.

Cost-effective selective deuteration of aromatic amino acid residues produces long-lived solution 1H NMR magnetization in proteins.

Solution NMR studies of large proteins are hampered by rapid signal decay due to short-range dipolar 1H-1H and 1H-13C interactions. These are attenuated by rapid rotation in methyl groups and by deuteration (2H), so selective 1H,13C-isotope labelling of methyl groups in otherwise perdeuterated proteins, combined with methyl transverse relaxation optimized spectroscopy (methyl-TROSY), is now standard for solution NMR of large protein systems > 25 kDa. For non-methyl positions, long-lived magnetization can be introduced as isolated 1H-12C groups. We have developed a cost-effective chemical synthesis for producing selectively deuterated phenylpyruvate and hydroxyphenylpyruvate. Feeding these amino acid precursors to E. coli in D2O, along with selectively deuterated anthranilate and unlabeled histidine, results in isolated and long-lived 1H magnetization in the aromatic rings of Phe (HD, HZ), Tyr (HD), Trp (HH2, HE3) and His (HD2 and HE1). We are additionally able to obtain stereoselective deuteration of Asp, Asn, and Lys amino acid residues using unlabeled glucose and fumarate as carbon sources and oxalate and malonate as metabolic inhibitors. Combining these approaches produces isolated 1H-12C groups in Phe, Tyr, Trp, His, Asp, Asn, and Lys in a perdeuterated background, which is compatible with standard 1H-13C labeling of methyl groups in Ala, Ile, Leu, Val, Thr, Met. We show that isotope labeling of Ala is improved using the transaminase inhibitor L-cycloserine, and labeling of Thr is improved through addition of Cys and Met, which are known inhibitors of homoserine dehydrogenase. We demonstrate the creation of long-lived 1H NMR signals in most amino acid residues using our model system, the WW domain of human Pin1, as well as the bacterial outer membrane protein PagP.

The endoplasmic reticulum kinase PERK interacts with the oxidoreductase ERO1 to metabolically adapt mitochondria.

Endoplasmic reticulum (ER) homeostasis requires molecular regulators that tailor mitochondrial bioenergetics to the needs of protein folding. For instance, calnexin maintains mitochondria metabolism and mitochondria-ER contacts (MERCs) through reactive oxygen species (ROS) from NADPH oxidase 4 (NOX4). However, induction of ER stress requires a quick molecular rewiring of mitochondria to adapt to new energy needs. This machinery is not characterized. We now show that the oxidoreductase ERO1⍺ covalently interacts with protein kinase RNA-like ER kinase (PERK) upon treatment with tunicamycin. The PERK-ERO1⍺ interaction requires the C-terminal active site of ERO1⍺ and cysteine 216 of PERK. Moreover, we show that the PERK-ERO1⍺ complex promotes oxidization of MERC proteins and controls mitochondrial dynamics. Using proteinaceous probes, we determined that these functions improve ER-mitochondria Ca2+ flux to maintain bioenergetics in both organelles, while limiting oxidative stress. Therefore, the PERK-ERO1⍺ complex is a key molecular machinery that allows quick metabolic adaptation to ER stress.

A Novel Predictive Model for Hospital Survival in Patients who are Critically Ill with Dialysis-Dependent AKI: A Retrospective Single-Center Exploratory Study.

Background: Mortality of patients who are critically ill with AKI initiated on RRT is very high. Identifying modifiable and unmodifiable clinical variables at dialysis start that are associated with hospital survival can help, not only in prognostication, but also in clinical triaging. Methods: A retrospective observational study was conducted on patients with AKI-D who were initiated on RRT in the medical and surgical intensive care units (ICUs) of a high-acuity academic medical center from January 2010 through December 2015. We excluded patients with suspected poisoning, ESKD, stage 5 CKD not on dialysis, or patients with AKI-D initiated on RRT outside of the ICU setting. The primary outcome was in-hospital mortality. Results: Of the 416 patients who were critically ill with AKI-D admitted to the medical (38%), surgical (41%), and cardiac (21%) ICUs, with nearly 75% on artificial organ support, the mean age 62.1±14.8 years, mean SOFA score was 11.8±4.3, dialysis was initiated using continuous RRT in 261 (63%) and intermittent hemodialysis in 155 (37%) patients. Incidence of survival to hospital discharge was 48%. Using multivariable logistic regression with stepwise backward elimination, a prognostic model was created that included the variables age, CKD, COPD, admission, and within 24 hours of the start SOFA score, refractory hyperkalemia and uremic encephalopathy as dialysis indications, BUN >100 mg/dl, serum creatinine, serum lactate, serum albumin, CRRT as initial modality, severe volume overload, and abdominal surgery. The model exhibited good calibration (goodness of fit test, P=0.83) and excellent discrimination (optimism-corrected C statistic 0.93). Conclusions: In this single-center, diverse, critically ill AKI-D population, a novel prognostic model that combined widely used ICU scores, clinical and biochemical data at dialysis start, and dialysis indication and modality, robustly predicted short-term survival. External validation is needed to prove the generalizability of the study findings.

Identification of proteins and cellular pathways targeted by 2-nitroimidazole hypoxic cytotoxins.

Tumour hypoxia negatively impacts therapy outcomes and continues to be a major unsolved clinical problem. Nitroimidazoles are hypoxia selective compounds that become entrapped in hypoxic cells by forming drug-protein adducts. They are widely used as hypoxia diagnostics and have also shown promise as hypoxia-directed therapeutics. However, little is known about the protein targets of nitroimidazoles and the resulting effects of their modification on cancer cells. Here, we report the synthesis and applications of azidoazomycin arabinofuranoside (N3-AZA), a novel click-chemistry compatible 2-nitroimidazole, designed to facilitate (a) the LC-MS/MS-based proteomic analysis of 2-nitroimidazole targeted proteins in FaDu head and neck cancer cells, and (b) rapid and efficient labelling of hypoxic cells and tissues. Bioinformatic analysis revealed that many of the 62 target proteins we identified participate in key canonical pathways including glycolysis and HIF1A signaling that play critical roles in the cellular response to hypoxia. Critical cellular proteins such as the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and the detoxification enzyme glutathione S-transferase P (GSTP1) appeared as top hits, and N3-AZA adduct formation significantly reduced their enzymatic activities only under hypoxia. Therefore, GAPDH, GSTP1 and other proteins reported here may represent candidate targets to further enhance the potential for nitroimidazole-based cancer therapeutics.

Mean local autocovariance provides robust and versatile choice of delay for reconstruction using frequently sampled flowlike data.

The first step in nonlinear time-series analysis can be selecting a delay for reconstruction. The most popular choices of this delay are the first zero of the autocovariance and the first minimum of the mutual information. An advantage of the first method arises from the robustness to noise of the autocovariance function, while an advantage of the second is that the first minimum of the mutual information provides a useful choice of delay for a wide range of nonlinear systems. We propose a method to choose a delay for frequently sampled flowlike data based on a mean local autocovariance function and compare its performance to methods based on the autocovariance and the mutual information. In addition, we compare the novel method to an established method based on cross-validatory mean-square errors of predictors corresponding to different choices of delay. The mean local autocovariance combines the versatility of the mutual information with some of the robustness to noise of the autocovariance.

Transplant nephrectomy with peritoneal window: Georgetown University experience.

OBJECTIVES: Transplant nephrectomy is a technically challenging procedure with high complication rates. Morbidity and mortality are mostly due to hemorrhage or infection and are reported to be 17-60% and 1-39%, respectively. The most common surgical technique for transplant nephrectomy is sub-capsular, extraperitoneal approach which may result in fluid accumulation and subsequent super-infection. We report that intraperitoneal approach, after assuring hemostasis of the transplant pedicle, allows for passive drainage, decreases hematoma formation and minimizes the subsequent infection risk in the nephrectomy bed. MATERIAL AND METHODS: From July 2009 to July 2014 a total of 38 transplant nephrectomies were performed using the intraperitoneal window technique at Georgetown University MedStar Transplant Institute (MGTI). Data was collected retrospectively. RESULTS: Average age at the time of transplant nephrectomy was 43.9 ± 14.3, and the majority were male (55.3%). Mean time to nephrectomy was 71.7 ± 67.4 months following transplantation. Indications for nephrectomy included pain, hematuria, fever, and recalcitrant rejection. Average operative time was 97.1 ± 28.9 minutes, average blood loss was 172.5 ± 213.6 mL. A total of 9 (24%) complications occurred. Postoperative blood transfusion was the most common complication (15.7%) followed by 2 (5.3%) re-interventions; one take back for hematoma and one percutaneous drain placement for symptomatic fluid collection. We had no infection, postoperative sepsis, ICU admissions, or mortality. CONCLUSION: Transplant nephrectomy with peritoneal window is a technique with better results compared to the literature. An opening between the transplant cavity and the peritoneum allows for passive drainage of fluid and minimizes the risk of hematoma and abscess formation. This approach does not add significant time to the operation, furthermore it may decrease morbidity and mortality by reducing overall complications, namely hematoma formation and infection, which overall decreases rates of re-interventions and length of hospital stay.

The OxyArm: a supplemental oxygen delivery device.

Facemasks and nasal cannulae are used to provide supplemental oxygen to patients in the postoperative period after general anesthesia. These devices are associated with several patient complications, including aspiration, hypercarbia, and mechanical trauma. A new device, the OxyArm, is designed to eliminate these problems. It is an "open oxygen" system that does not require physical contact with the patient's face. In this clinical study we evaluated the OxyArm in the immediate postoperative period. Sixty patients received supplemental oxygen via the OxyArm for the first 8 min after tracheal extubation after general anesthesia. Oxygen saturation values were continuously recorded during 3 4-min time periods: 1) while breathing oxygen through an endotracheal tube before tracheal extubation, 2) while breathing oxygen delivered by the OxyArm at 4 L/min 4 min after tracheal extubation, and 3) while breathing oxygen delivered by the OxyArm at 2 L/min 8 min after tracheal extubation. There were no significant differences in oxygen saturation among the three time periods and no patient experienced an oxygen desaturation event less than 88%. Patients and clinicians praised the OxyArm for its comfort and ease of use, allowing nursing facial care without interrupting oxygen therapy. We conclude that the OxyArm delivers adequate levels of oxygen for most patients during the early postoperative period.

A Low-Cost, Open-Source, Compliant Hand for Enabling Sensorimotor Control for People with Transradial Amputations.

In this paper, we describe the design and implementation of a low-cost, open-source prosthetic hand that enables both motor control and sensory feedback for people with transradial amputations. We integrate electromyographic pattern recognition for motor control along with contact reflexes and sensory substitution to provide feedback to the user. Compliant joints allow for robustness to impacts. The entire hand can be built for around $550. This low cost makes research and development of sensorimotor prosthetic hands more accessible to researchers worldwide, while also being affordable for people with amputations in developing nations. We evaluate the sensorimotor capabilites of our hand with a subject with a transradial amputation. We show that using contact reflexes and sensory substitution, when compared to standard myoelectric prostheses that lack these features, improves grasping of delicate objects like an eggshell and a cup of water both with and without visual feedback. Our hand is easily integrated into standard sockets, facilitating long-term testing of sensorimotor capabilities.

Pre-empting Antibody-Mediated Rejection: A Program of DSA Monitoring and Treatment Can Effectively Prevent Antibody Mediated Rejection.

Antibody-mediated rejection (AMR) remains a problem without a reliable treatment in the care of kidney transplant patients. We proposed and tested a program of screening for donor specific antibodies (DSA) to initiate treatment of patients before AMR was detected and to prevent its occurrence. Starting in April 2012, we stratified patients into high-, medium-, and low-risk groups for the development of DSA and instituted a program of screening for and treatment of these antibodies. We used a historic control group of patients transplanted at our center as a comparator and looked at rates of DSA testing and development as well as rates of development of AMR, cell-mediated rejection, and graft loss. 614 patients were transplanted under the protocol compared with 266 patients in the control group. Length of follow-up was similar in both groups. The group undergoing DSA screening had lower rates of DSA development (17.6% versus 24.8%, p=0.016) and that DSA was found at a significantly earlier time post-transplant (147 versus 248 days, p=0.02). Incidence of AMR was dramatically lower in the screened group (1.3% versus 8.6%, p<0.0001) with no grafts lost due to AMR. AMR was found to occur at an average of 181 days post-transplant. Rates of acute cellular rejection did not decrease in a manner similar to AMR rates. In conclusion, a program of universal risk-stratified DSA testing in kidney transplant patients can dramatically reduce rates of AMR and virtually eliminate graft loss due to AMR.