Mycobacterium smegmatis putative Holliday junction resolvases RuvC and RuvX play complementary roles in the processing of branched DNA structures.

In eubacteria, Holliday junction (HJ) resolvases (HJRs) are crucial for faithful segregation of newly replicated chromosomes, homologous recombination, and repair of stalled/collapsed DNA replication forks. However, compared with the Escherichia coli HJRs, little is known about their orthologs in mycobacterial species. A genome-wide analysis of Mycobacterium smegmatis identified two genes encoding putative HJRs, namely RuvC (MsRuvC) and RuvX (MsRuvX); but whether they play redundant, overlapping, or distinct roles remains unknown. Here, we reveal that MsRuvC exists as a homodimer while MsRuvX as a monomer in solution, and both showed high-binding affinity for branched DNAs compared with unbranched DNA species. Interestingly, the DNA cleavage specificities of MsRuvC and MsRuvX were found to be mutually exclusive: the former efficiently promotes HJ resolution, in a manner analogous to the Escherichia coli RuvC, but does not cleave other branched DNA species; whereas the latter is a versatile DNase capable of cleaving a variety of branched DNA structures, including 3' and 5' flap DNA, splayed-arm DNA and dsDNA with 3' and 5' overhangs but lacks the HJ resolution activity. Point mutations in the RNase H-like domains of MsRuvC and MsRuvX pinpointed critical residues required for their DNA cleavage activities and also demonstrated uncoupling between DNA-binding and DNA cleavage activities. Unexpectedly, we found robust evidence that MsRuvX possesses a double-strand/single-strand junction-specific endonuclease and ssDNA exonucleolytic activities. Combined, our findings highlight that the RuvC and RuvX DNases play distinct complementary, and not redundant, roles in the processing of branched DNA structures in M. smegmatis.

Impact of delay in the diagnosis on the severity of celiac disease.

BACKGROUND AND AIM: Celiac disease (CeD) has now become a global disease with a worldwide prevalence of 0.67%. Despite being a common disease, CeD is often not diagnosed and there is a significant delay in its diagnosis. We reviewed the impact of the delay in the diagnosis on the severity of manifestations of CeD. METHODS: We reviewed clinical records of 726 consecutive patients with CeD from the Celiac Clinic database and the National Celiac Disease Consortium database. We extracted specific data including the demographics, symptoms at presentation, time of onset of symptoms, time to diagnosis from the onset of the symptoms, and relevant clinical data including fold-rise in anti-tissue transglutaminase antibody (IgA anti-tTG Ab) and severity of villous and crypt abnormalities as assessed using modified Marsh classification. RESULTS: The median duration between the onset of symptoms and the diagnosis of CeD was 27 months (interquartile range 12-60 months). A longer delay in the diagnosis of CeD from the onset of symptoms was associated with lower height for age, lower hemoglobin, higher fold rise in IgA Anti tTG titers, and higher severity of villous and crypt abnormalities. About 18% of patients presented with predominantly non-gastrointestinal complaints and had a longer delay in the diagnosis of CeD. CONCLUSIONS: There is a significant delay in the diagnosis of CeD since the onset of its symptoms. The severity of celiac disease increases with increasing delay in its diagnosis. There is a need to keep a low threshold for the diagnosis of CeD in appropriate clinical settings.

Validation of no-biopsy pathway for the diagnosis of celiac disease in Asian adults: a multicenter retrospective study.

BACKGROUND AND AIM: While European Society of Pediatric Gastroenterology Hepatology and Nutrition advocates a no-biopsy pathway for the diagnosis of celiac disease (CeD) in children if IgA anti-tissue transglutaminase antibody (anti-tTG ab) titer is ≥10-fold upper limit of normal (ULN) and have a positive IgA anti-endomysial antibody (EMA); the data for anti-tTG Ab titer-based diagnosis of CeD in adults is still emerging. We planned to validate if IgA anti-tTG Ab titer ≥10-fold predicts villous abnormalities of modified Marsh grade ≥2 in Asian adult patients with CeD. METHODS: We recruited 937 adult patients with positive anti-tTG Ab from two databases, including AIIMS Celiac Clinic and Indian National Biorepository. The diagnosis of definite CeD was made on the basis of a positive anti-tTG Ab and the presence of villous abnormalities of modified Marsh grade ≥2. RESULTS: Of 937 adult patients with positive anti-tTG Ab, 889 (91.2%) showed villous abnormalities of modified Marsh grade ≥2. Only 47.6% of 889 adults with CeD had anti- tTG Ab titers of ≥10-fold. The positive predictive value (PPV) and specificity of anti tTG Ab titer ≥10-fold for predicting modified Marsh grade ≥2 were 99.8% and 98%, respectively. At anti-tTG Ab titer ≥11-fold, specificity and PPV were 100% for predicting villous abnormalities of modified Marsh grade ≥2. CONCLUSIONS: Approximately 50% of adults with CeD may benefit from the no biopsy pathway, reducing the health burden and risks of gastroscopy/anesthesia.

Paving the way to environment-friendly greener anesthesia.

Health-care settings have an important responsibility toward environmental health and safety. The operating room is a major source of environmental pollution within a hospital. Inhalational agents and nitrous oxide are the commonly used gases during general anesthesia for surgeries, especially in the developing world. These greenhouse gases contribute adversely to the environmental health both inside the operating room and in the outside atmosphere. Impact of these anesthetic agents depends on the total consumption, characteristics of individual agents, and gas flows, with higher levels increasing the environmental adverse effects. The inimical impact of nitrous oxide is higher due to its longer atmospheric half-life and potential for destruction of the ozone layer. Anesthesiologist of today has a choice in the selection of anesthetic agents. Prudent decisions will help in mitigating environmental pollution and contributing positively to a greener planet. Therefore, a shift from inhalational to intravenous-based technique will reduce the carbon footprint of anesthetic agents and their impact on global climate. Propofol forms the mainstay of intravenous anesthesia technique and is a proven drug for anesthetic induction and maintenance. Anesthesiologists should appreciate growing concerns about the role of inhalational anesthetics on the environment and join the cause of environmental responsibility. In this narrative review, we revisit the pharmacological and pharmacokinetic considerations, clinical uses, and discuss the merits of propofol-based intravenous anesthesia over inhalational anesthesia in terms of environmental effects. Increased awareness about the environmental impact and adoption of newer, versatile, and user-friendly modalities of intravenous anesthesia administration will pave the way for greener anesthesia practice.

Mapping fluid structuration to flow enhancement in nanofluidic channels.

Fluid flow in miniature devices is often characterized by a boundary "slip" at the wall, as opposed to the classical paradigm of a "no-slip" boundary condition. While the traditional mathematical description of fluid flow as expressed by the differential forms of mass and momentum conservation equations may still suffice in explaining the resulting flow physics, one inevitable challenge against a correct quantitative depiction of the flow velocities from such considerations remains in ascertaining the correct slip velocity at the wall in accordance with the complex and convoluted interplay of exclusive interfacial phenomena over molecular scales. Here, we report an analytic engine that applies combined physics-based and data-driven modeling to arrive at a quantitative depiction of the interfacial slip via a molecular-dynamics-trained machine learning algorithm premised on fluid structuration at the wall. The resulting mapping of the system parameters to a single signature data that bridges the molecular and continuum descriptions is envisaged to be a preferred computationally inexpensive route as opposed to expensive multi-scale or molecular simulations that may otherwise be inadequate to resolve the flow features over experimentally tractable physical scales.

Therapeutic plasma-exchange improves short-term, but not long-term, outcomes in patients with acute-on-chronic liver failure: A propensity score-matched analysis.

BACKGROUND: Acute-on-chronic liver failure (ACLF) is associated with a high short-term mortality rate in the absence of liver transplantation. The role of therapeutic plasma exchange (TPE) in improving the outcomes of ACLF and acute decompensation (AD) is unclear. In this retrospective analysis, we aimed to determine the impact of TPE on mortality in patients with ACLF. METHODS: ACLF patients receiving TPE with standard medical treatment (SMT) were propensity score matched (PSM) with those receiving SMT alone (1:1) for sex, grades of ACLF, CLIF C ACLF scores, and the presence of hepatic encephalopathy. The primary outcomes assessed were mortality at 30 and 90 days. Survival analysis was performed using Kaplan Meier survival curves. RESULTS: A total of 1151 patients (ACLF n = 864 [75%], AD [without organ failure] n = 287 [25%]) were included. Of the patients with ACLF (n = 864), grade 1, 2, and 3 ACLF was present in 167 (19.3%), 325 (37.6%), and 372 (43.0%) patients, respectively. Thirty-nine patients received TPE and SMT, and 1112 patients received only SMT. On PSM analysis, there were 38 patients in each group (SMT plus TPE vs SMT alone). In the matched cohort, the 30-days mortality was lower in the TPE arm compared to SMT (21% vs 50%, P = .008), however, the 90-day mortality was not significantly different between the two groups (36.8% vs 52.6%, P = .166); HR, 0.82 (0.44-1.52), P = .549. CONCLUSION: TPE improves short-term survival in patients with ACLF, but has no significant impact on long-term outcomes. Randomized control trials are needed to obtain a robust conclusion in this regard.

Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier.

The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. Early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the "spring predictability barrier" remains a great challenge for long-lead-time (over 6 mo) forecasting. To overcome this barrier, here we develop an analysis tool, System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Niño 3.4 region. When applying this tool to several near-surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Niño and the previous calendar year's SysSampEn (complexity). We show that this correlation allows us to forecast the magnitude of an El Niño with a prediction horizon of 1 y and high accuracy (i.e., root-mean-square error = 0.23° C for the average of the individual datasets forecasts). For the 2018 El Niño event, our method forecasted a weak El Niño with a magnitude of 1.11±0.23° C. Our framework presented here not only facilitates long-term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.

GPU-based, interactive exploration of large spatiotemporal climate networks.

This paper introduces the Graphics Processing Unit (GPU)-based tool Geo-Temporal eXplorer (GTX), integrating a set of highly interactive techniques for visual analytics of large geo-referenced complex networks from the climate research domain. The visual exploration of these networks faces a multitude of challenges related to the geo-reference and the size of these networks with up to several million edges and the manifold types of such networks. In this paper, solutions for the interactive visual analysis for several distinct types of large complex networks will be discussed, in particular, time-dependent, multi-scale, and multi-layered ensemble networks. Custom-tailored for climate researchers, the GTX tool supports heterogeneous tasks based on interactive, GPU-based solutions for on-the-fly large network data processing, analysis, and visualization. These solutions are illustrated for two use cases: multi-scale climatic process and climate infection risk networks. This tool helps one to reduce the complexity of the highly interrelated climate information and unveils hidden and temporal links in the climate system, not available using standard and linear tools (such as empirical orthogonal function analysis).

Exploring the Perceived Educational Impact of COVID-19 on Postgraduate Training in Oncology.

This study sought to report the degree to which postgraduate trainees in radiation oncology perceive their education has been impacted by the COVID-19 pandemic. A cross-sectional online survey was administered from June to July 2020 to trainee members of the Canadian Association of Radiation Oncology (CARO) (n = 203). Thirty-four trainees responded with a 17% response rate. Just under half of participants indicated that COVID-19 had a negative/very negative impact on training (n = 15; 46%). The majority agreed/strongly agreed that they feared family/loved ones would contract COVID-19 (n = 29, 88%), felt socially isolated from friends and family because of COVID-19 (n = 23, 70%), and had difficulty concentrating on tasks because of concerns about COVID-19 (n = 17, 52%). Changes that had a negative/very negative impact on learning included limitations to travel and networking (n = 31; 91%) and limited patient contact (n = 19; 58%). Virtual follow-ups (n = 25: 76%) and in-patient care activities (n = 12; 36%) increased. Electives were cancelled in province (n = 10; 30%), out-of-province (n = 16; 49%), and internationally (n = 15; 46%). Teaching from staff was moderately reduced to completely suppressed (n = 23, 70%) and teaching to medical students was moderately reduced to completely suppressed (n = 27, 82%). Significant changes to radiation oncology training were wrought by the pandemic, and roughly half of trainees perceive that these changes had a negative impact on training. Innovations in training delivery are needed to adapt to these new changes.

A complex network approach to study the extreme precipitation patterns in a river basin.

The quantification of spatial propagation of extreme precipitation events is vital in water resources planning and disaster mitigation. However, quantifying these extreme events has always been challenging as many traditional methods are insufficient to capture the nonlinear interrelationships between extreme event time series. Therefore, it is crucial to develop suitable methods for analyzing the dynamics of extreme events over a river basin with a diverse climate and complicated topography. Over the last decade, complex network analysis emerged as a powerful tool to study the intricate spatiotemporal relationship between many variables in a compact way. In this study, we employ two nonlinear concepts of event synchronization and edit distance to investigate the extreme precipitation pattern in the Ganga river basin. We use the network degree to understand the spatial synchronization pattern of extreme rainfall and identify essential sites in the river basin with respect to potential prediction skills. The study also attempts to quantify the influence of precipitation seasonality and topography on extreme events. The findings of the study reveal that (1) the network degree is decreased in the southwest to northwest direction, (2) the timing of 50th percentile precipitation within a year influences the spatial distribution of degree, (3) the timing is inversely related to elevation, and (4) the lower elevation greatly influences connectivity of the sites. The study highlights that edit distance could be a promising alternative to analyze event-like data by incorporating event time and amplitude and constructing complex networks of climate extremes.

Effect of Body Position and Size of Pleural Effusion on Oxygenation Status in Spontaneously Breathing Patients with Unilateral Pleural Effusion.

Abvims and Dr Rml Hospital, Delhi In unilateral pleural effusion it is necessary to find the best position for unilateral pleural effusion so that recovery can be hastened. There is scarcity of literature on the effect of body positional variations on oxygenation, and current guidelines do not address the necessity. The objective of this study is to assess the effect of body position and size of pleural effusion on oxygenation status in spontaneously breathing patients with unilateral pleural effusion. MATERIAL: This was a hospital based observational cross-sectional study having a sample size of 90 patients of unilateral pleural effusion and on the basis of severity, they were divided into 2 classes- small and large. Ipsilateral and Contralateral oxygenation were analysed separately in small and large effusions using Mann-Whitney and Wilcoxon signed rank test and final analysis was made using SPSS software. OBSERVATION: It was observed that oxygenation was better when patient lies on contralateral side in small effusion (PaO2 82.4±8.83 vs 85.01±8.24 p value <0.0001) while in cases of large effusion, oxygenation was better on ipsilateral side of effusion (82.35±10.4 vs 78.06±9.92, p value<0.0001). No significant difference was noted in case of PaCO2 levels in small and large effusion in ipsilateral and contralateral position. It was also noted that there was a significant difference in oxygen saturation. CONCLUSION: There is significant effect of body position and size of pleural effusion on oxygenation status in spontaneously breathing patient with unilateral pleural effusion.

A prospective observational study on changes in endo-tracheal tube cuff pressure and its correlation with airway pressures during various stages of robotic pelvic surgeries.

Background and Aims: Robotic surgeries often require a relatively long duration of pneumo-peritoneum and trendelenburg position which may accentuate changes in endo-tracheal tube (ETT) cuff pressure leading to pressure related complications. The aim of this study was to analyze changes in ETT cuff pressures during various stages of pneumo-peritoneum and surgical positioning and its correlation with airway pressure changes. Material and Methods: A prospective observational study was planned after approval of institutional review board on 60 patients undergoing elective robotic pelvic surgery requiring head down position. Baseline cuff pressure was adjusted to 25 cm H2O. ETT cuff pressure, peak airway pressure and end tidal CO2 (ETCO2) was measured at various time intervals before and after pneumo-peritoneum and head down. Ventilatory parameters were kept fixed after baseline setting. Those requiring any change were excluded. Pearson's coefficient was used for correlation and ANOVA for trend of parameters at different time intervals (P value <0.05 was considered significant). Results: Baseline cuff pressure after manual inflation was 46.2 ± 17.4 cm H2O. Significant correlation was observed between change in cuff pressure and increase in peak airway pressure at the end of the surgery (r = 0.4, P < 0.05). Serial measurements of ETT cuff pressure, peak airway pressure and ETCO2 were significantly increased compared to baseline (P < 0.05). Conclusion: Significant increases in ETT cuff pressure may be seen in robotic surgeries, with a positive correlation between change in cuff pressure and increase in airway pressures. Objective adjusted measurement of cuff pressure and airway pressures is recommended for such surgeries.

Acute mesenteric ischemia in COVID-19: Case report and current understanding.

INTRODUCTION: Since the outbreak of coronavirus disease 2019 (COVID-19) in December 2019, various thrombotic complications have been frequently reported in patients with infection. Acute mesenteric ischemia (AMI) is a rare but life-threatening complication in this disease, which requires early recognition and prompt treatment. CASE PRESENTATION: We report two cases of COVID-19-related AMI. Both patients underwent emergency laparotomy for small bowel ischemia. The first patient received prompt intervention and was discharged 5 days after surgery. The second patient presented late to the hospital and succumbed 72 h after surgery. CONCLUSION: These two cases highlight the importance of high suspicion, early recognition, and prompt treatment in patients with abdominal symptoms related to COVID-19.

Severe coarctation of the aorta without a severely elevated echocardiographic pressure gradient: a case highlighting the importance of aortic Doppler flow profile.

An asymptomatic 5-year-old male was diagnosed with severe coarctation of the aorta despite normal peak flow velocity with pathology identified on the basis of Doppler flow profile.

Application of Endotracheal Tube Cuff Pressure Monitoring during Percutaneous Dilatational Tracheostomy: A Novel Technique.

Endotracheal tube (ETT) cuff pressure monitoring during percutaneous dilatational tracheostomy (PDT) procedure is an easy-to-use innovative addition to the standard blind technique in a resource-limited setting. This technique can be carried out without disconnecting the breathing circuit, resulting in a lower risk of infectious aerosol generation. HOW TO CITE THIS ARTICLE: Mohammad H, Jain G, Agarwal A, Kausar S, Sama S. Application of Endotracheal Tube Cuff Pressure Monitoring during Percutaneous Dilatational Tracheostomy: A Novel Technique. Indian J Crit Care Med 2021;25(9):1040-1041.

Arterial Blood Gas as a Predictor of Mortality in COVID Pneumonia Patients Initiated on Noninvasive Mechanical Ventilation: A Retrospective Analysis.

Background: The alveolar-arterial oxygen (A-a) gradient measures the difference between the oxygen concentration in alveoli and the arterial system, which has considerable clinical utility. Materials and methods: It was a retrospective, observational cohort study involving the analysis of patients diagnosed with acute COVID pneumonia and required noninvasive mechanical ventilation (NIV) over a period of 3 months. The primary objective was to investigate the utility of the A-a gradient (pre-NIV) as a predictor of 28-day mortality in COVID pneumonia. The secondary objective included the utility of other arterial blood gas (ABG) parameters (pre-NIV) as a predictor of 28-day mortality. The outcome was also compared between survivors and nonsurvivors. The outcome variables were analyzed by receiver-operating characteristic (ROC) curve, Youden index, and regression analysis. Results: The optimal criterion for A-a gradient to predict 28-day mortality was calculated as ≤430.43 at a Youden index of 0.5029, with the highest area under the curve (AUC) of 0.755 (p <0.0001). On regression analysis, the odds ratio for the A-a gradient was 0.99. A significant difference was observed in ABG predictors, including PaO2, PaCO2, A-a gradient, AO2, and arterial-alveolar (a-A) (%) among nonsurvivors vs survivors (p-value <0.001). The vasopressor requirement, need for renal replacement therapy, total parenteral requirement, and blood transfusion were higher among nonsurvivors; however, a significant difference was achieved with the vasopressor need (p <0.001). Conclusion: This study demonstrated that the A-a gradient is a significant predictor of mortality in patients initiated on NIV for worsening respiratory distress in COVID pneumonia. All other ABG parameters also showed a significant AUC for predicting 28-day mortality, although with variable sensitivity and specificity. Key messages: COVID-19 pneumonia shows an initial presentation with type 1 respiratory failure with increased A-a gradient, while a subsequent impending type 2 respiratory failure requires invasive ventilation. A significant difference was observed in ABG predictors, including PaO2, PaCO2, A-a gradient, AO2, and a-A (%) among nonsurvivors vs survivors. (p-value <0.001). The vasopressor requirement, need for renal replacement therapy, total parenteral requirement, and blood transfusion need were higher among nonsurvivors than survivors; however, a significant difference was achieved with the vasopressor need (p <0.001). How to cite this article: Gupta B, Jain G, Chandrakar S, Gupta N, Agarwal A. Arterial Blood Gas as a Predictor of Mortality in COVID Pneumonia Patients Initiated on Noninvasive Mechanical Ventilation: A Retrospective Analysis. Indian J Crit Care Med 2021;25(8):866-871.

Forecasting of extreme flood events using different satellite precipitation products and wavelet-based machine learning methods.

An accurate and timely forecast of extreme events can mitigate negative impacts and enhance preparedness. Real-time forecasting of extreme flood events with longer lead times is difficult for regions with sparse rain gauges, and in such situations, satellite precipitation could be a better alternative. Machine learning methods have shown promising results for flood forecasting with minimum variables indicating the underlying nonlinear complex hydrologic system. Integration of machine learning methods in extreme event forecasting motivates us to develop reliable flood forecasting models that are simple, accurate, and applicable in data scare regions. In this study, we develop a forecasting method using the satellite precipitation product and wavelet-based machine learning models. We test the proposed approach in the flood-prone Vamsadhara river basin, India. The validation results show that the proposed method is promising and has the potential to forecast extreme flood events with longer lead times in comparison with the other benchmark models.

Wavelet entropy-based evaluation of intrinsic predictability of time series.

Intrinsic predictability is imperative to quantify inherent information contained in a time series and assists in evaluating the performance of different forecasting methods to get the best possible prediction. Model forecasting performance is the measure of the probability of success. Nevertheless, model performance or the model does not provide understanding for improvement in prediction. Intuitively, intrinsic predictability delivers the highest level of predictability for a time series and informative in unfolding whether the system is unpredictable or the chosen model is a poor choice. We introduce a novel measure, the Wavelet Entropy Energy Measure (WEEM), based on wavelet transformation and information entropy for quantification of intrinsic predictability of time series. To investigate the efficiency and reliability of the proposed measure, model forecast performance was evaluated via a wavelet networks approach. The proposed measure uses the wavelet energy distribution of a time series at different scales and compares it with the wavelet energy distribution of white noise to quantify a time series as deterministic or random. We test the WEEM using a wide variety of time series ranging from deterministic, non-stationary, and ones contaminated with white noise with different noise-signal ratios. Furthermore, a relationship is developed between the WEEM and Nash-Sutcliffe Efficiency, one of the widely known measures of forecast performance. The reliability of WEEM is demonstrated by exploring the relationship to logistic map and real-world data.

The Radiation Oncology Mentorship Initiative: Analysis of a Formal Mentoring Initiative for Medical Students Interested in Radiation Oncology.

The importance of mentorship in medicine and its impact on academic and professional development has been widely studied. However, mentorship for medical students in the field of radiation oncology is limited. Our radiation oncology department developed a formal medical student mentorship program in 2004. This program included both clinical and research mentoring pathways. Our study aims to gain feedback and perspective from former medical student participants who subsequently entered into a radiation oncology residency program. An anonymous survey was sent to 22 former students in the mentorship program from 2005 to 2016 who entered a radiation oncology residency program. The survey included Likert scales (1-5), multiple choice, strength category rankings, and free responses. Data was compiled and analyzed with Qualtrics data software. The survey response rate was 100%. Seventeen (77.3%) participants reported that the mentorship program strongly affected their career choice and a majority reported that their research experience strongly (45.5%) or moderately affected (31.8%) their career choice. Fourteen (63.6%) respondents reported that the mentorship program was very effective and 8 (36.4%) reported it as being effective. Eighteen (81.8%) respondents reported that mentorship was extremely important to their career. Students participating in the research pathway also reported improvement in valuable skills such as presentations, abstract writing, manuscript writing, statistical analysis, and coordination with colleagues. A total of 66.7% of attending radiation oncologists who previously participated in this program now practice in an academic setting. Our institution successfully developed a formalized mentorship program for medical students interested in radiation oncology. Participants in this program reported high levels of satisfaction and emphasized the importance of mentorship in the development of valuable research competencies and on their overall career path. This program can serve as a model for future mentorship initiative in medical school.

Disentangling the multi-scale effects of sea-surface temperatures on global precipitation: A coupled networks approach.

The oceans and atmosphere interact via a multiplicity of feedback mechanisms, shaping to a large extent the global climate and its variability. To deepen our knowledge of the global climate system, characterizing and investigating this interdependence is an important task of contemporary research. However, our present understanding of the underlying large-scale processes is greatly limited due to the manifold interactions between essential climatic variables at different temporal scales. To address this problem, we here propose to extend the application of complex network techniques to capture the interdependence between global fields of sea-surface temperature (SST) and precipitation (P) at multiple temporal scales. For this purpose, we combine time-scale decomposition by means of a discrete wavelet transform with the concept of coupled climate network analysis. Our results demonstrate the potential of the proposed approach to unravel the scale-specific interdependences between atmosphere and ocean and, thus, shed light on the emerging multiscale processes inherent to the climate system, which traditionally remain undiscovered when investigating the system only at the native resolution of existing climate data sets. Moreover, we show how the relevant spatial interdependence structures between SST and P evolve across time-scales. Most notably, the strongest mutual correlations between SST and P at annual scale (8-16 months) concentrate mainly over the Pacific Ocean, while the corresponding spatial patterns progressively disappear when moving toward longer time-scales.