Cancer genomes harbor a catalog of somatic mutations. The type and genomic context of these mutations depend on their causes and allow their attribution to particular mutational signatures. Previous work has shown that mutational signature activities change over the course of tumor development, but investigations of genomic region variability in mutational signatures have been limited. Here, we expand upon this work by constructing regional profiles of mutational signature activities over 2,203 whole genomes across 25 tumor types, using data aggregated by the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium. We present GenomeTrackSig as an extension to the TrackSig R package to construct regional signature profiles using optimal segmentation and the expectation-maximization (EM) algorithm. We find that 426 genomes from 20 tumor types display at least one change in mutational signature activities (changepoint), and 306 genomes contain at least one of 54 recurrent changepoints shared by seven or more genomes of the same tumor type. Five recurrent changepoint locations are shared by multiple tumor types. Within these regions, the particular signature changes are often consistent across samples of the same type and some, but not all, are characterized by signatures associated with subclonal expansion. The changepoints we found cannot strictly be explained by gene density, mutation density, or cell-of-origin chromatin state. We hypothesize that they reflect a confluence of factors including evolutionary timing of mutational processes, regional differences in somatic mutation rate, large-scale changes in chromatin state that may be tissue type-specific, and changes in chromatin accessibility during subclonal expansion. These results provide insight into the regional effects of DNA damage and repair processes, and may help us localize genomic and epigenomic changes that occur during cancer development. Author summary: Somatic mutations accumulate through cancer development. These mutations are the result of DNA damage and DNA repair deficiencies; in some cases these mutation sources can be recovered by attributing mutations to different mutational signatures. Mutational signatures describe patterns of co-occurring substitution types (e.g., C to A) that are associated with various mutagenic processes. For example, UV radiation causes CpC to TpT substitutions, and smoking mutagens are associated with C to A substitution. Previous work has shown that mutational signature activities in cancer genomes change over the course of a cancer’s development. Less is known about how their activities change across the entire genome, which is relevant for understanding the regional effects of DNA damage and repair deficiencies. We have developed a bioinformatic tool GenomeTrackSig, which uses a segmentation algorithm to estimate mutational signature activities across large chromosomal domains and identify changepoints where activities vary. We apply GenomeTrackSig to 2,203 cancer genomes and find that 426 contain changepoints. Some changepoints recur across multiple samples of the same type, and some also exhibit activity tradeoffs between signatures active in either early or late cancer development. Changepoints cannot be explained well by genomic factors that typically contribute to mutation rate variation. We hypothesize that they are driven by a confluence of factors, including large-scale changes in chromatin state that may occur over cancer evolution.

Development of irrigation technologies and agricultural water management systems holds significant potential to improve productivity and reduce vulnerability to climate change. Our study dealt with the behavior of irrigation water productivity, partial nutrient balance and grain yield of wheat under the application of different irrigation water management technologies in the Koga irrigation scheme in Ethiopia. For our analysis, we considered three nutrient fluxes entering and leaving farmers’ fields. Our experimental design had three irrigation blocks with three different irrigation water management practices (wetting front detector, Chameleon soil moisture sensor and farmers’ practice as control) on three farm plots replicated in each block. To calculate irrigation water productivity and grain yield of wheat, the amount of irrigation water applied and the agronomic attributes of wheat yield were recorded during the irrigation period. Further, three input and output variables were considered to determine the partial nutrient balances of nitrogen (N), phosphorus (P) and potassium (K). The results showed that the amount of irrigation water used was 33% and 22% less with a wetting front detector and Chameleon sensors, respectively, compared to the farmers’ practice. The wetting front detector (WFD) and Chameleon sensor (CHS) treatments gave a 20% and 15.8% grain yield increment, respectively, compared to the farmers’ practice plot. The partial nutrient balances of N and K were negative for the wetting front detector and chameleon sensor practices while it was positive for P in the control (farmers’ practice) treatment. We conclude that irrigation water management with appropriate technologies can improve yield, water productivity and the nutrient utilization. However, further research needs to be conducted on the suitability of irrigation management technologies to achieve full nutrient balance.

Identifying host factors that influence infectious disease transmission is an important step toward developing interventions to reduce disease incidence. Recent advances in methods for reconstructing infectious disease transmission events using pathogen genomic and epidemiological data open the door for investigation of host factors that affect onward transmission. While most transmission reconstruction methods are designed to work with densely sampled outbreaks, these methods are making their way into surveillance studies, where the fraction of sampled cases with sequenced pathogens could be relatively low. Surveillance studies that use transmission event reconstruction then use the reconstructed events as response variables (i.e., infection source status of each sampled case) and use host characteristics as predictors (e.g., presence of HIV infection) in regression models. We use simulations to study estimation of the effect of a host factor on probability of being an infection source via this multi-step inferential procedure. Using TransPhylo—a widely-used method for Bayesian estimation of infectious disease transmission events—and logistic regression, we find that low sensitivity of identifying infection sources leads to dilution of the signal, biasing logistic regression coefficients toward zero. We show that increasing the proportion of sampled cases improves sensitivity and some, but not all properties of the logistic regression inference. Application of these approaches to real world data from a population-based TB study in Botswana fails to detect an association between HIV infection and probability of being a TB infection source. We conclude that application of a pipeline, where one first uses TransPhylo and sparsely sampled surveillance data to infer transmission events and then estimates effects of host characteristics on probabilities of these events, should be accompanied by a realistic simulation study to better understand biases stemming from imprecise transmission event inference. Author summary: Factors that affect infectious disease transmission are poorly understood, which impede efforts to prevent the spread of infectious diseases. Recently, software packages have been developed to infer transmission histories of infectious disease outbreaks using data from infectious disease genetics and epidemiology. These software packages have been used as part of methods to identify individual characteristics that affect infectious disease transmission. We used computer simulation to explore whether a statistical pipeline using the software package TransPhylo can successfully identify individual risk factors for being an infection source in a realistic public health setting where only a small proportion of pathogens are sequenced. We simulated tuberculosis (TB) outbreaks with different odds of being an infection source for TB transmission between people living with and without HIV. We found that the TransPhylo-based pipeline consistently underestimated the odds ratio for the association between HIV and being an infection source for TB transmission. We then applied this method to data from a TB study from Botswana and found no evidence of an association between HIV and being an infection source for TB transmission. Identification of transmission risk factors may be difficult in settings with low sampling proportion for genetic data.

Neural circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control spontaneous network activity. We show that in firing-rate networks in the balanced state, external control of recurrent dynamics, i.e., the suppression of internally-generated chaotic variability, strongly depends on correlations in the input. A distinctive feature of balanced networks is that, because common external input is dynamically canceled by recurrent feedback, it is far more difficult to suppress chaos with common input into each neuron than through independent input. To study this phenomenon, we develop a non-stationary dynamic mean-field theory for driven networks. The theory explains how the activity statistics and the largest Lyapunov exponent depend on the frequency and amplitude of the input, recurrent coupling strength, and network size, for both common and independent input. We further show that uncorrelated inputs facilitate learning in balanced networks. Author summary: Information in the brain is processed by a deeply-layered structure of local recurrent neural circuits. Recurrent neural networks often exhibit spontaneous irregular activity patterns that arise generically through the disordered interactions between neurons. Understanding under which conditions one circuit can control the activity patterns in another circuit and suppress spontaneous, chaotic fluctuations is crucial to unravel information flow and learning input-output tasks.

Background: Noncommunicable diseases (NCDs), especially hypertension and diabetes, are rapidly rising in sub-Saharan Africa, necessitating health systems transformations. In Tanzania, current policies aim to improve control of hypertension and diabetes, but information is still needed to assess the gaps in treatment. Methods and findings: We conducted a cross-sectional household survey of 784 adults in two districts in Tanzania from December 2020 to January 2021, capturing the cascade-of-care for hypertension and diabetes. The ages of the respondents ranged from 18 to 89 years. Of those screened positive for these conditions, we measured the proportion in each step of the cascades: awareness, care engagement, treatment, and control. We conducted multivariable logistic regression analyses for all four steps along the hypertension care cascade with the independent variables of social health protection schemes, and prior diagnosis of comorbid diabetes, and demographic information. In our sample, of the 771 who had their blood pressure measured, 41% (95% confidence interval (CI): 38% to 44%) were screened positive for hypertension, and of the 707 who had their blood sugar measured, 6% (95% CI: 4% to 8%) were screened positive for diabetes. Of those with hypertension, 43% (95% CI: 38% to 49%) had a prior diagnosis, 25% (95% CI: 21% to 31%) were engaged in care, 21% (95% CI: 3% to 25%) were on treatment, and 11% (95% CI: 8% to 15%) were controlled. Of the 42 respondents with diabetes, 80% (95% CI: 69% to 93%) had a prior diagnosis. The diabetes care cascade had much less drop-off, so 66% of those with diabetes (95% CI: 52% to 82%) were engaged in care and on treatment, and 48% (95% CI: 32% to 63%) had their diabetes controlled at the point of testing. Healthcare fee exemptions were independently associated with higher odds of being previously diagnosed (OR 5.81; 95% CI [1.98 to 17.10] p < 0.005), engaged in care (OR 4.71; 95% CI [1.59 to 13.90] p 0.005), and retained in treatment (OR 2.93; 95% CI [1.03 to 8.35] p < 0.05). Prior diagnosis of comorbid diabetes was highly associated with higher odds of being engaged in care for hypertension (OR 3.26; 95% CI [1.39 to 7.63] p < 0.005). The two primary limitations of this study were reliance on screening at a single time point only of people available at the village at the time of the sample and dependence on self-report for to inform the three cascade steps of prior diagnosis, healthcare visits for engagement in care, and treatment use. Conclusions: The high burden of hypertension and low levels of control in our study underscores the importance of improving the awareness and treatment of hypertension. The differences in the care cascades for hypertension and diabetes demonstrates that chronic NCD treatment is possible in this setting, but efforts will be needed across the entire care cascade to improve hypertension control. Dr. Brianna Osetinsky and colleagues report a cross sectional evaluation of care cascades for the delivery of hypertension and diabetes care in rural districts of Tanzania. Why was this study done?: What did the researchers do and find?: What do these findings mean?:

Awareness of HIV serostatus helps individuals calibrate behaviour or link to care. Globally, young people (15-24years) contribute over 30% of new HIV infections. Despite progress in enhancing access to HIV services, HIV testing among young people in Uganda is below target. We determined the prevalence and factors influencing HIV testing among young people in a peri-urban district with the highest proportion of young people. We conducted a nested explanatory sequential mixed-methods study from March to May 2019 in Wakiso district. We used stratified cluster random sampling to select 397 rural and 253 urban young people from eight parishes. We collected data using questionnaires and subsequently conducted in-depth interviews with 16 purposively selected survey participants. The prevalence of testing for HIV was 80.2%. Young people related their decisions about HIV testing to self-evaluation of their risk and perceived ability to manage the consequences of a positive result. Participants reported high levels of support for HIV testing from peers, partners, and family members. They perceived health facilities as confusing, distant, expensive, and staffed by judgmental, older health workers as barriers. They felt that mobile testing points solved some of these problems, but introduced less privacy and greater confidentiality concerns. The prevalence of HIV testing among young people in Wakiso district was low compared to the UNAIDS 2030 target but among the highest in sub-Saharan Africa. Community-based programs resolve many concerns about testing at health facilities. However, there is a need to make these programs more comfortable and private.

Identifying cell clusters is a critical step for single-cell transcriptomics study. Despite the numerous clustering tools developed recently, the rapid growth of scRNA-seq volumes prompts for a more (computationally) efficient clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite graph representation algorithm, Secuer enjoys reduced runtime and memory usage over one order of magnitude for datasets with more than 1 million cells. Meanwhile, Secuer also achieves better or comparable accuracy than competing methods in small and moderate benchmark datasets. Furthermore, we showcase that Secuer can also serve as a building block for a new consensus clustering method, Secuer-consensus, which again improves the runtime and scalability of state-of-the-art consensus clustering methods while also maintaining the accuracy. Overall, Secuer is a versatile, accurate, and scalable clustering framework suitable for small to ultra-large single-cell clustering tasks. Author summary: Recently, single-cell RNA sequencing (scRNA-seq) has enabled profiling of thousands to millions of cells, spurring the development of efficient clustering algorithms for large or ultra-large datasets. In this work, we developed an ultrafast clustering method, Secuer, for small to ultra-large scRNA-seq data. Using simulation and real datasets, we demonstrated that Secuer yields high accuracy, while saving runtime and memory usage by orders of magnitude, and that it can be efficiently scaled up to ultra-large datasets. Additionally, with Secuer as a subroutine, we proposed Secuer-consensus, a consensus clustering algorithm. Our results show that Secuer-consensus performs better in terms of clustering accuracy and runtime.

Consumption of edible insects is a potential solution to the growing need for protein. However, the wild harvested edible insects’ value chain faces several challenges including limited knowledge on indigenous practices in the harvesting and processing and lack of information on roles of the different actors in the chain. A case study of Gonimbrasia belina, colloquially referred to as the ’mopane worm’, was conducted to understand and identify determinants of participation in the value chain of the edible caterpillar. A cross sectional study was conducted in Gwanda (a rural district in Zimbabwe) to (a) understand the indigenous knowledge on harvesting and processing methods, (b) explore value addition and the traditional beliefs surrounding the utilisation of the mopane worm. Results showed that consumers (81.7%), and harvesters (76.6%) were the main actors in the mopane worm value chain. Using the Ordinary Least Squares (OLS) model, the following were shown to be determinants of participation in the mopane worm value chain: (a) gender (b) household size (c) marital status (d) religion and (e) household assets. Two primary processing methods of harvested mopane worm were distinguished i.e., boiling and roasting on ambers. Results showed lack of diversity in mopane worm-based products. Current culturally acceptable processing techniques need improvement and standardization to support sustainable mopane worm processing while optimising nutrient bio-accessibility.

Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method’s performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods. Author summary: Cancer progression is an evolutionary process where somatic mutations in so-called driver genes provide the harboring cells with certain selective advantages. Identifying the interplay among the driver genes is critical for understanding how cancer evolves. In this paper, we introduce a method for analyzing cohorts of tumors. Our approach is based on a novel probabilistic model, which can identify the temporal order of mutations in driver genes, and how they may exhaust each other’s selective advantages. We introduce an efficient likelihood calculation procedure and build an MCMC algorithm for making inferences based on our model. Our computationally efficient inference algorithm enables us to work with hundreds of genes and thousands of tumors. Using a broad set of synthetic data experiments, we demonstrate the performance of our inference algorithm in various scenarios. We also present our analyses of several biological datasets. Our results agree with a set of well-known relations among the driver genes and suggest new interesting such relationships.

The COVAX program aims to provide global equitable access to life-saving vaccines. Despite calls for increased sharing, vaccine protectionism has limited progress towards vaccine sharing goals. For example, as of April 2022 only ~20% of the population in Africa had received at least one COVID-19 vaccine dose. Here we use a two-nation coupled epidemic model to evaluate optimal vaccine-sharing policies given a selfish objective: in which countries with vaccine stockpiles aim to minimize fatalities in their own population. Computational analysis of a suite of simulated epidemics reveal that it is often optimal for a donor country to share a significant fraction of its vaccine stockpile with a recipient country that has no vaccine stockpile. Sharing a vaccine stockpile reduces the intensity of outbreaks in the recipient, in turn reducing travel-associated incidence in the donor. This effect is intensified as vaccination rates in a donor country decrease and epidemic coupling between countries increases. Critically, vaccine sharing by a donor significantly reduces transmission and fatalities in the recipient. Moreover, the same computational framework reveals the potential use of hybrid sharing policies that have a negligible effect on fatalities in the donor compared to the optimal policy while significantly reducing fatalities in the recipient. Altogether, these findings provide a self-interested rationale for countries to consider sharing part of their vaccine stockpiles.