Características clínicas y epidemiológicas de la Mpox en población asegurada de La Libertad, 2022 / Clinical and epidemiological characteristics of mpox among the insured population of La Libertad, 2022

RESUMEN Objetivo: Describir las características clínicas y epidemiológicas de la viruela símica (Mpox) en la población asegurada de La Libertad del Seguro Social de Salud (EsSalud). Materiales y métodos: Estudio descriptivo, cuyos datos se recolectaron de las fichas clínico-epidemiológicas e historias clínicas; se consideró casos según la sintomatología y el resultado positivo de la prueba PCR. Las variables de estudio fueron síntomas y signos, duración de la enfermedad, antecedentes clínicos, sexo, edad, orientación sexual, lugar de contacto con la persona con la Mpox. Se calcularon frecuencias absolutas y relativas e intervalos de confianza. Resultados: La Mpox se presentó en la población asegurada entre el 15 de julio y el 31 de diciembre del 2022, y se notificaron 48 casos. Las características clínicas fueron fiebre (54,17 %), astenia y linfadenopatía (52,08 %) (cuya localización fue inguinal en el 25 %, cervical en el 12 % y axilar en el 5 %), mialgia y dolor de espalda (43,75 %), dolor de garganta (37,50 %) y escalofríos (5 %), exantema polimórfico y de forma centrífuga (100 %); además, existieron complicaciones (6,25 %) y hubo una persona fallecida (letalidad de 6,25 %). Se presentó inmunodepresión por VIH en 23 casos (47,92 %); antecedente de sífilis, 4 casos (8,33 %); herpes genital, 3 casos (6,25 %); verrugas genitales, 2 casos (4,17 %). Afectó a 47 hombres (97,92 %), entre ellos a homosexuales (58,33 %), 13 heterosexuales (27,08 %) y 7 bisexuales (14,58 %). Diez de ellos tuvieron contacto con personas con la Mpox (20,83 %) en su domicilio, 7 (14,59 %) en el trabajo, 5 (10,42 %) en una fiesta y 2 (4,17 %) en un bar. Conclusiones: La Mpox se manifestó principalmente en hombres homosexuales y bisexuales no vacunados contra la viruela humana. Los principales síntomas fueron fiebre, astenia y linfadenopatía con predominio inguinal. Además, el exantema fue polimórfico en todos los casos, la enfermedad duró de 17 a 45 días, las complicaciones fueron excepcionales, el 50 % de casos tuvieron inmunodepresión por VIH y la letalidad fue de 6,25 %.

ABSTRACT Objective: To describe the clinical and epidemiological characteristics of monkeypox (mpox) among the insured population of La Libertad at Seguro Social de Salud (EsSalud Social Health Insurance System). Materials and methods: A descriptive study, whose data were collected from clinical-epidemiological records and medical records; the cases with symptoms and positive PCR results were considered. The study variables were signs and symptoms, duration of the disease, medical history, sex, age, sexual orientation and place of contact with someone with mpox. Absolute and relative frequencies and confidence intervals were calculated. Results: Mpox was developed by the insured population between July 15 and December 31, 2022, and 48 cases were reported. The clinical characteristics were fever (54.17 %), asthenia and lymphadenopathy (52.08 %) (in the inguinal [25 %], cervical [12 %] and axillary [5 %] areas), myalgia and back pain (43.75 %), sore throat (37.50 %), chills (5 %) and polymorphous and centrifugal rash (100 %). In addition, there were complications (6.25 %) and one person died (case fatality rate 6.25 %). HIV immunosuppression, history of syphilis, genital herpes and genital warts occurred in 23 (47.92 %), four (8.33%), three (6.25 %) and two (4.17 %) cases, respectively. It affected 47 men (97.92 %), including 28 homosexuals (58.33 %), 13 heterosexuals (27.08 %) and seven bisexuals (14.58 %). Ten of them had contact with someone with mpox at home (20.83 %), seven at work (14.59 %), five at a party (10.42 %) and two at a bar (4.17 %). Conclusions: Mpox occurred mainly in homosexual and bisexual men not vaccinated against human smallpox. The most common symptoms were fever, asthenia and lymphadenopathy, mainly in the inguinal area. Moreover, all cases developed polymorphous rash, the duration of the disease was 17 to 45 days, complications were exceptional, 50 % of the cases had HIV immunosuppression and the case fatality rate was 6.25 %.

Identifying keystone species in microbial communities using deep learning.

Previous studies suggested that microbial communities can harbour keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. Here we propose a data-driven keystone species identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep-learning model using microbiome samples collected from this habitat. The well-trained deep-learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data and applied DKI to analyse real data. We found that those taxa with high median keystoneness across different communities display strong community specificity. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.

Assembly archetypes in ecological communities.

An instrumental discovery in comparative and developmental biology is the existence of assembly archetypes that synthesize the vast diversity of organisms' body plans-from legs and wings to human arms-into simple, interpretable and general design principles. Here, we combine a novel mathematical formalism based on category theory with experimental data to show that similar 'assembly archetypes' exist at the larger organization scale of ecological communities when assembling a species pool across diverse environmental contexts, particularly when species interactions are highly structured. We applied our formalism to clinical data discovering two assembly archetypes that differentiate between healthy and unhealthy human gut microbiota. The concept of assembly archetypes and the methods to synthesize them can pave the way to discovering the general assembly principles of the ecological communities we observe in nature.

Identifying keystone species in microbial communities using deep learning.

Previous studies suggested that microbial communities harbor keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. This is mainly due to our limited knowledge of microbial dynamics and the experimental and ethical difficulties of manipulating microbial communities. Here, we propose a Data-driven Keystone species Identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep learning model using microbiome samples collected from this habitat. The well-trained deep learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data generated from a classical population dynamics model in community ecology. We then applied DKI to analyze human gut, oral microbiome, soil, and coral microbiome data. We found that those taxa with high median keystoneness across different communities display strong community specificity, and many of them have been reported as keystone taxa in literature. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.

Predicting microbiome compositions from species assemblages through deep learning.

Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment or their hosts' well-being. Rationally managing these microbial communities requires improving our ability to predict how different species assemblages affect the final species composition of the community. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. To overcome this challenge, we present a deep learning framework that automatically learns the map between species assemblages and community compositions from training data only, without knowing any of the above processes. First, we systematically validate our framework using synthetic data generated by classical population dynamics models. Then, we apply our framework to data from in vitro and in vivo microbial communities, including ocean and soil microbiota, Drosophila melanogaster gut microbiota, and human gut and oral microbiota. We find that our framework learns to perform accurate out-of-sample predictions of complex community compositions from a small number of training samples. Our results demonstrate how deep learning can enable us to understand better and potentially manage complex microbial communities.

Structure-based identification of sensor species for anticipating critical transitions.

Ecological systems can undergo sudden, catastrophic changes known as critical transitions. Anticipating these critical transitions remains challenging in systems with many species because the associated early warning signals can be weakly present or even absent in some species, depending on the system dynamics. Therefore, our limited knowledge of ecological dynamics may suggest that it is hard to identify those species in the system that display early warning signals. Here, we show that, in mutualistic ecological systems, it is possible to identify species that early anticipate critical transitions by knowing only the system structure-that is, the network topology of plant-animal interactions. Specifically, we leverage the mathematical theory of structural observability of dynamical systems to identify a minimum set of "sensor species," whose measurement guarantees that we can infer changes in the abundance of all other species. Importantly, such a minimum set of sensor species can be identified by using the system structure only. We analyzed the performance of such minimum sets of sensor species for detecting early warnings using a large dataset of empirical plant-pollinator and seed-dispersal networks. We found that species that are more likely to be sensors tend to anticipate earlier critical transitions than other species. Our results underscore how knowing the structure of multispecies systems can improve our ability to anticipate critical transitions.

Características de pacientes hospitalizados con COVID-19 en la red asistencial La Libertad-EsSalud, 2020 / Characteristics of patients hospitalized for COVID-19 in the La Libertad-EsSalud healthcare network, 2020

RESUMEN Objetivo: Describir las características de los pacientes hospitalizados con COVID-19 en la red asistencial La Libertad-EsSalud, 2020. Materiales y métodos: Estudio descriptivo. Los datos se recolectaron de las fichas clínico-epidemiológicas y de las historias clínicas de los pacientes que se hospitalizaron entre el 15 de marzo y el 17 de agosto del 2020. Las variables consideradas en la investigación fueron edad, sexo, comorbilidad, estancia hospitalaria, uso de ventilación mecánica, días con ventilación mecánica, fallecidos y pacientes de alta. Se calcularon frecuencias absolutas y relativas, y el valor de p. Resultados: El estudio incluyó a 2093 pacientes hospitalizados con COVID-19, el promedio de edad fue 58 años, y el 63,21 % eran hombres. El 39,99 % tenía comorbilidades. La media de tiempo de hospitalización fue 8 días. El 9,79 % de los pacientes ingresó a la unidad de cuidados intensivos y permaneció por 20,40 días, en promedio. El 9,56 % requirió ventilación mecánica. Los fallecidos fueron el 40,28 % de todos los pacientes; y el 54,50 % de los enfermos que requirieron ventilación mecánica murieron. El 49,74 % de pacientes fue dado de alta. Conclusiones: El mayor número de pacientes era de sexo masculino. La edad media de los participantes fue 58 años. Las principales comorbilidades fueron hipertensión arterial, diabetes y obesidad. El tiempo promedio de hospitalización fue 8,02 días. El 9,79 % de los pacientes estuvieron en la unidad de cuidados intensivos; y el 9,56 % requirió ventilación mecánica. El 40,28 % de los enfermos fallecieron, y de los pacientes con ventilación mecánica, murieron el 54,50 %. Los hospitalizados que salieron de alta fueron el 49,74 %.

ABSTRACT Objective: To describe the characteristics of patients hospitalized for COVID-19 in the La Libertad-EsSalud healthcare network, 2020. Materials and methods: A descriptive study. The data was collected from the clinical epidemiological records and medical records of patients hospitalized from March 15 to August 17, 2020. The research variables were age, sex, comorbidity, hospital stay, use of mechanical ventilation, number of days on mechanical ventilation, number of deaths and number of discharged patients. Absolute and relative frequencies and the p value were calculated. Results: The study population consisted of 2,093 patients hospitalized for COVID-19, whose average age was 58 years. Sixty- one point two three percent (61.23 %) were males, 39.99 % had comorbidities, the mean hospital stay was 8 days, 9.79 % were admitted to the ICU and spent 20.40 days on average, 9.56 % required mechanical ventilation, 40.28 % of the total number of patients died, 54.50 % of those who received mechanical ventilation died, and 49.74 % were discharged. Conclusions: The average age of the patients was 58 years, most of whom were males. The main comorbidities were high blood pressure, diabetes and obesity. The average hospital stay was 8.02 days, 9.79 % of the patients were admitted to the ICU, 9.56 % required mechanical ventilation, 40.28 % died, 54.50 % of those who received mechanical ventilation died, and 49.74 % were discharged.

Coexistence holes characterize the assembly and disassembly of multispecies systems.

A central goal of ecological research has been to understand the limits on the maximum number of species that can coexist under given constraints. However, we know little about the assembly and disassembly processes under which a community can reach such a maximum number, or whether this number is in fact attainable in practice. This limitation is partly due to the challenge of performing experimental work and partly due to the lack of a formalism under which one can systematically study such processes. Here, we introduce a formalism based on algebraic topology and homology theory to study the space of species coexistence formed by a given pool of species. We show that this space is characterized by ubiquitous discontinuities that we call coexistence holes (that is, empty spaces surrounded by filled space). Using theoretical and experimental systems, we provide direct evidence showing that these coexistence holes do not occur arbitrarily-their diversity is constrained by the internal structure of species interactions and their frequency can be explained by the external factors acting on these systems. Our work suggests that the assembly and disassembly of ecological systems is a discontinuous process that tends to obey regularities.

Supporting "Bleeders" and "Billers": How Safety-Net Administrators Mitigate Provider Burnout During the COVID-19 Pandemic and Beyond.

Organizational factors impacting burnout have been underexplored among providers in low-income, minority-serving, safety-net settings. Our team interviewed 14 health care administrators, serving as key decision makers in Federally Qualified Health Center primary care clinics. Using a semistructured interview guide, we explored burnout mitigation strategies and elements of organizational culture and practice. Transcribed interviews were coded and analyzed using the Braun and Clark (2006) Thematic Analysis method. Mission-Driven Ethos to Mitigate Provider Burnout emerged as the primary theme with 2 categories: (1) Promoting the Mission: "Bleeders" and (2) Competing Priorities: "Billers." These categories represent various properties and reflect administrators' use of organizational mission statement as a driver of staff recruitment, training, retention, and stratification. Data collection occurred before and during the COVID-19 global pandemic, as such additional themes associated with administrative behaviors during a prolonged, clinical crisis provide insight into possible strategies that may mitigate burnout in this setting.

A simple criterion to design optimal non-pharmaceutical interventions for mitigating epidemic outbreaks.

For mitigating the COVID-19 pandemic, much emphasis is made on implementing non-pharmaceutical interventions to keep the reproduction number below one. However, using that objective ignores that some of these interventions, like bans of public events or lockdowns, must be transitory and as short as possible because of their significant economic and societal costs. Here, we derive a simple and mathematically rigorous criterion for designing optimal transitory non-pharmaceutical interventions for mitigating epidemic outbreaks. We find that reducing the reproduction number below one is sufficient but not necessary. Instead, our criterion prescribes the required reduction in the reproduction number according to the desired maximum of disease prevalence and the maximum decrease of disease transmission that the interventions can achieve. We study the implications of our theoretical results for designing non-pharmaceutical interventions in 16 cities and regions during the COVID-19 pandemic. In particular, we estimate the minimal reduction of each region's contact rate necessary to control the epidemic optimally. Our results contribute to establishing a rigorous methodology to design optimal non-pharmaceutical intervention policies for mitigating epidemic outbreaks.

Transmisión de COVID-19 en el personal de salud del hospital Víctor Lazarte Echegaray de Trujillo / Transmission of COVID-19 among the health personnel of the Hospital Víctor Lazarte Echegaray in Trujillo

RESUMEN Objetivo: Describir las características de la transmisión de COVID-19 en el personal de salud del hospital Víctor Lazarte Echegaray de Trujillo. Materiales y métodos: Estudio de tipo descriptivo. Los datos fueron recolectados mediante entrevistas, fichas clínico- epidemiológicas e historias clínicas. Se identificó al personal que atendió a los pacientes con COVID-19 y a quienes adquirieron la infección y desarrollaron síntomas. Los procedimientos realizados en los pacientes fueron registrados en una lista. Los casos se describen según tipo de personal de salud y semana epidemiológica. Las frecuencias absolutas y relativas, así como la tasa de ataque, fueron determinadas. El Comité de Investigación y Ética del hospital aprobó el estudio. Resultados: Seis pacientes hospitalizados tuvieron el diagnóstico confirmado de COVID-19. Todos ellos fueron atendidos por 45 trabajadores de la salud en procedimientos como hemodiálisis, ventilación mecánica, intubación orotraqueal, nebulización y endoscopía alta sin los equipos de protección personal. A consecuencia de ello, 38 individuos resultaron infectados y presentaron un cuadro clínico caracterizado por malestar general, tos, fiebre y dolor de garganta. Los médicos y las enfermeras fueron los trabajadores de salud más afectados. Conclusiones: La transmisión intrahospitalaria de COVID-19 en el personal de salud fue evidente. Los trabajadores más afectados fueron los médicos y las enfermeras. La tasa de ataque fue de 84,44 %.

ABSTRACT Objective: To describe the characteristics of COVID-19 transmission among the health personnel. Materials and methods: A descriptive research was conducted. Data were collected through interviews, clinical- epidemiological records and medical records. The personnel who treated COVID-19 patients, and those who acquired the infection and developed symptoms were identified. The medical procedures undergone by the patients were listed. The cases were described according to the type of health personnel and epidemiological week. The absolute and relative frequencies, as well as the attack rate, were determined. The research was approved by the hospital's research and ethics committee. Results: The diagnosis of COVID-19 was confirmed in six hospitalized patients. All of them were treated by 45 health workers in procedures such as hemodialysis, mechanical ventilation, orotracheal intubation, nebulization and upper endoscopy with no personal protective equipment. As a result, 38 individuals were infected and developed symptoms, including malaise, cough, fever and sore throat. The most affected health workers were doctors and nurses. Conclusions: In-hospital transmission of COVID-19 was evident in the health personnel, affecting most frequently doctors and nurses. The attack rate accounted for 84.44 %.

Generalizing game-changing species across microbial communities.

Microbes form multispecies communities that play essential roles in our environment and health. Not surprisingly, there is an increasing need for understanding if certain invader species will modify a given microbial community, producing either a desired or undesired change in the observed collection of resident species. However, the complex interactions that species can establish between each other and the diverse external factors underlying their dynamics have made constructing such understanding context-specific. Here we integrate tractable theoretical systems with tractable experimental systems to find general conditions under which non-resident species can change the collection of resident communities-game-changing species. We show that non-resident colonizers are more likely to be game-changers than transients, whereas game-changers are more likely to suppress than to promote resident species. Importantly, we find general heuristic rules for game-changers under controlled environments by integrating mutual invasibility theory with in vitro experimental systems, and general heuristic rules under changing environments by integrating structuralist theory with in vivo experimental systems. Despite the strong context-dependency of microbial communities, our work shows that under an appropriate integration of tractable theoretical and experimental systems, it is possible to unveil regularities that can then be potentially extended to understand the behavior of complex natural communities.

An ecological framework to understand the efficacy of fecal microbiota transplantation.

Human gut microbiota plays critical roles in physiology and disease. Our understanding of ecological principles that govern the dynamics and resilience of this highly complex ecosystem remains rudimentary. This knowledge gap becomes more problematic as new approaches to modifying this ecosystem, such as fecal microbiota transplantation (FMT), are being developed as therapeutic interventions. Here we present an ecological framework to understand the efficacy of FMT in treating conditions associated with a disrupted gut microbiota, using the recurrent Clostridioides difficile infection as a prototype disease. This framework predicts several key factors that determine the efficacy of FMT. Moreover, it offers an efficient algorithm for the rational design of personalized probiotic cocktails to decolonize pathogens. We analyze data from both preclinical mouse experiments and a clinical trial of FMT to validate our theoretical framework. The presented results significantly improve our understanding of the ecological principles of FMT and have a positive translational impact on the rational design of general microbiota-based therapeutics.

Brote de escabiosis en el Hospital Víctor Lazarte Echegaray, Trujillo, 2017 / Scabies outbreak at the Hospital Víctor Lazarte Echegaray, Trujillo, 2017

Objetivo: Describir las características de un brote de escabiosis y evaluar el impacto de las medidas de control.Materiales y métodos: Estudio epidemiológico descriptivo y prospectivo, realizado en un hospital de referencia, con 220 camas de hospitalización. Los datos se recolectaron a través de visitas diarias al Servicio de Medicina, entrevistas al personal asistencial y revisión de las historias clínicas. En la descripción del brote se consideran las variables epidemiológicas de persona, lugar y tiempo. Se aplicó estadística descriptiva, y se determinaron las frecuencias absolutas y relativas y la tasa de ataque. Se solicitó consentimiento informado a los pacientes y al personal asistencial, se les explicó sobre el beneficio de la investigación. La intervención consistió en medidas de aislamiento y precauciones de transmisión por contacto, se enfatizó el uso de guantes no estériles.Resultados: El brote ocurrió desde la semana epidemiológica 29 hasta la 37 del 2017. El caso primario fue un paciente varón de 93 años de edad con sarna noruega, hospitalizado por demencia de Alzheimer. El cuadro se presentó en 9 enfermeras, 7 médicos residentes, 6 técnicos de enfermería, 1 interno de medicina y 5 pacientes hospitalizados. Conclusiones: El brote fue de fuente propagada, con duración de 9 semanas epidemiológicas; y afectó enfermeras, médicos residentes, internos de medicina y a pacientes hospitalizados. La tasa de ataque fue del 40 %. Las medidas de prevención y control fueron aislamiento del caso primario y de los pacientes, higiene de manos antes y después de la atención, uso de guantes no estériles, uso de bata, artículos de uso clínico exclusivos para cada paciente, la ropa de los pacientes con sarna fue manipulada con guantes y en bolsas cerradas, limpieza de superficies, exclusión de la atención clínica de los trabajadores de la salud con sarna hasta las 24 horas de iniciado el tratamiento, y la vigilancia epidemiológica. El tratamiento médico consistió en loción de permetrina al 5 % por tres días. Se recomienda aplicar normas de bioseguridad en la atención de todos los pacientes, sobre todo cuando se hospitalizan con lesiones dermatológicas. Las medidas que se implementaron deben tenerse en cuenta en la prevención y control de este tipo de brote.

Objective: To describe the characteristics of a scabies outbreak and assess the effect of the control measures.Materials and methods: A descriptive and prospective epidemiological study was conducted at a reference hospital with 220 inpatient beds. Data was collected by visiting the Medicine Service on a daily basis, interviewing the healthcare personnel and examining the medical records. The description of the outbreak includes epidemiological variables such as subject, place and time. Descriptive statistics was used to determine the absolute and relative frequencies, as well as the attack rate. Patients and healthcare personnel were requested to sign an informed consent and received information about the benefits of the research. The intervention consisted of isolation measures and contact transmission-based precautions. The use of non-sterile gloves was emphasized.Results: The outbreak took place between the epidemiological weeks 29 and 37 in the year 2017. The index case was a 93-year-old male patient who had Norwegian scabies and was admitted to the hospital due to Alzheimer's disease. The scabies occurred in nine female nurses, seven residents, six nurse technicians, one intern y five inpatients. Conclusions: This propagated-source outbreak lasted nine epidemiological weeks; and affected nurses, residents, interns and inpatients. The attack rate was 40 %. The prevention and control measures were isolation of the index case and patients, hand hygiene before and after patient care, use of non-sterile gloves, use of scrubs, use of individual medical supplies for each patient, handling of clothing from scabies-infected patients in sealed bags and using gloves, surface cleaning, withdrawal of scabies-infected health workers from their duties until 24 hours of treatment onset, and epidemiological surveillance. Medical treatment was 5 % permethrin lotion for three days. Use of biosafety standards is recommended for patient care, especially when patients are admitted to the hospital with skin lesions. The implemented measures must be considered to prevent and control this type of outbreak.

Revealing Complex Ecological Dynamics via Symbolic Regression.

Understanding the dynamics of complex ecosystems is a necessary step to maintain and control them. Yet, reverse-engineering ecological dynamics remains challenging largely due to the very broad class of dynamics that ecosystems may take. Here, this challenge is tackled through symbolic regression, a machine learning method that automatically reverse-engineers both the model structure and parameters from temporal data. How combining symbolic regression with a "dictionary" of possible ecological functional responses opens the door to correctly reverse-engineering ecosystem dynamics, even in the case of poorly informative data, is shown. This strategy is validated using both synthetic and experimental data, and it is found that this strategy is promising for the systematic modeling of complex ecological systems.

A theoretical framework for controlling complex microbial communities.

Microbes form complex communities that perform critical roles for the integrity of their environment or the well-being of their hosts. Controlling these microbial communities can help us restore natural ecosystems and maintain healthy human microbiota. However, the lack of an efficient and systematic control framework has limited our ability to manipulate these microbial communities. Here we fill this gap by developing a control framework based on the new notion of structural accessibility. Our framework uses the ecological network of the community to identify minimum sets of its driver species, manipulation of which allows controlling the whole community. We numerically validate our control framework on large communities, and then we demonstrate its application for controlling the gut microbiota of gnotobiotic mice infected with Clostridium difficile and the core microbiota of the sea sponge Ircinia oros. Our results provide a systematic pipeline to efficiently drive complex microbial communities towards desired states.

Robust qualitative estimation of time-varying contact rates in uncertain epidemics.

We will inevitably face new epidemics where the lack of long time-series data and the uncertainty about the outbreak dynamics make difficult to obtain quantitative predictions. Here we present an algorithm to qualitatively infer time-varying contact rates from short time-series data, letting us predict the start, relative magnitude and decline of epidemic outbreaks. Using real time-series data of measles, dengue, and the current zika outbreak, we demonstrate our algorithm can outperform existing algorithms based on estimating reproductive numbers.

Mapping the ecological networks of microbial communities.

Mapping the ecological networks of microbial communities is a necessary step toward understanding their assembly rules and predicting their temporal behavior. However, existing methods require assuming a particular population dynamics model, which is not known a priori. Moreover, those methods require fitting longitudinal abundance data, which are often not informative enough for reliable inference. To overcome these limitations, here we develop a new method based on steady-state abundance data. Our method can infer the network topology and inter-taxa interaction types without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka-Volterra model, our method can infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental data sets. Our method represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.

"It Feels Good to Know That Someone Cares".

INTRODUCTION: Diabetes is the leading cause of death in Hispanic communities. Self-management is an important part of diabetes care, and diabetes self-management education (DSME) aims to teach the skills necessary for preventing and delaying complication. However, DSME is underutilized. The purpose of this study was to explore Hispanic adults' motivations for attending a DSME class to identify effective strategies for promoting class participation and retention. METHOD: Nineteen adults participated in seven focus groups conducted in Spanish. Discussions were audio-recorded, transcribed, and translated. Transcripts were content coded by two coders to create a thematic coding scheme. RESULTS: Five main themes emerged as motivations for attendance: (1) frustration with physiological changes, (2) desire to "do better" because of family experience with death/complications from diabetes, (3) free access to information that is unattainable elsewhere, (3) a way to take control, and (4) group setting offered valued emotional and informational support as well as peer support for those who were uncomfortable discussing diabetes with family or lived with family who do not support lifestyle changes. CONCLUSIONS: Gaining diabetes self-management knowledge only partly explains the perceived value of classes. Culturally relevant content and appealing to the social supportive aspects of groups may encourage participation.

Fundamental limitations of network reconstruction from temporal data.

Inferring properties of the interaction matrix that characterizes how nodes in a networked system directly interact with each other is a well-known network reconstruction problem. Despite a decade of extensive studies, network reconstruction remains an outstanding challenge. The fundamental limitations governing which properties of the interaction matrix (e.g. adjacency pattern, sign pattern or degree sequence) can be inferred from given temporal data of individual nodes remain unknown. Here, we rigorously derive the necessary conditions to reconstruct any property of the interaction matrix. Counterintuitively, we find that reconstructing any property of the interaction matrix is generically as difficult as reconstructing the interaction matrix itself, requiring equally informative temporal data. Revealing these fundamental limitations sheds light on the design of better network reconstruction algorithms that offer practical improvements over existing methods.