Aquatic systems worldwide can exist in multiple ecosystem states (i.e., a recurring collection of biological and chemical attributes), and effectively characterizing multidimensionality will aid protection of desirable states and guide rehabilitation. The Upper Mississippi River System is composed of a large floodplain river system spanning 2200 km and multiple federal, state, tribal and local governmental units. Multiple ecosystem states may occur within the system, and characterization of the variables that define these ecosystem states could guide river rehabilitation. We coupled a long-term (30-year) highly dimensional water quality monitoring dataset with multiple topological data analysis (TDA) techniques to classify ecosystem states, identify state variables, and detect state transitions over 30 years in the river to guide conservation. Across the entire system, TDA identified five ecosystem states. State 1 was characterized by exceptionally clear, clean, and cold-water conditions typical of winter (i.e., a clear-water state); State 2 had the greatest range of environmental conditions and contained most the data (i.e., a status-quo state); and States 3, 4, and 5 had extremely high concentrations of suspended solids (i.e., turbid states, with State 5 as the most turbid). The TDA mapped clear patterns of the ecosystem states across several riverine navigation reaches and seasons that furthered ecological understanding. State variables were identified as suspended solids, chlorophyll a, and total phosphorus, which are also state variables of shallow lakes worldwide. The TDA change detection function showed short-term state transitions based on seasonality and episodic events, and provided evidence of gradual, long-term changes due to water quality improvements over three decades. These results can inform decision making and guide actions for regulatory and restoration agencies by assessing the status and trends of this important river and provide quantitative targets for state variables. The TDA change detection function may serve as a new tool for predicting the vulnerability to undesirable state transitions in this system and other ecosystems with sufficient data. Coupling ecosystem state concepts and TDA tools can be transferred to any ecosystem with large data to help classify states and understand their vulnerability to state transitions. Author summary: The Upper Mississippi River System in the USA is a large, complex ecosystem that extends from about St. Paul, Minnesota, to the confluence with the Ohio River near Cairo, Illinois, and up the Illinois River. The system benefits from a water quality monitoring program for which consistent data have been collected since about 1986. We used a set of mathematical tools called ‘topological data analyses’ to help identify common water quality conditions or ‘states’ of this important river system. We also explored when and why the states have changed over the past 27 years. The tools identified five common water quality states, distinguished by nutrients and clear waters or muddy and turbid waters. State changes were commonly detected as seasonal transitions in the higher latitude reaches. The state changes also showed that water quality had improved or declined gradually over many years, depending on the river reach. River reaches named Pool 4 and Pool 8 had become clearer over time. Pool 13 became muddier over time with several undesirable state changes to a turbid-state. Three other river reaches were consistently muddy. Our study is a good example of how these topological data analysis tools may help others managing ecosystems by improving communications about what states exist and which are desirable. Coupling these tools with long-term data can also alert ecosystem managers to the ongoing, complex changes and vulnerability to help prioritize ecosystem rehabilitation.

It has been 49 years since the last discovery of a new virus family in the model yeast Saccharomyces cerevisiae. A large-scale screen to determine the diversity of double-stranded RNA (dsRNA) viruses in S. cerevisiae has identified multiple novel viruses from the family Partitiviridae that have been previously shown to infect plants, fungi, protozoans, and insects. Most S. cerevisiae partitiviruses (ScPVs) are associated with strains of yeasts isolated from coffee and cacao beans. The presence of partitiviruses was confirmed by sequencing the viral dsRNAs and purifying and visualizing isometric, non-enveloped viral particles. ScPVs have a typical bipartite genome encoding an RNA-dependent RNA polymerase (RdRP) and a coat protein (CP). Phylogenetic analysis of ScPVs identified three species of ScPV, which are most closely related to viruses of the genus Cryspovirus from the mammalian pathogenic protozoan Cryptosporidium parvum. Molecular modeling of the ScPV RdRP revealed a conserved tertiary structure and catalytic site organization when compared to the RdRPs of the Picornaviridae. The ScPV CP is the smallest so far identified in the Partitiviridae and has structural homology with the CP of other partitiviruses but likely lacks a protrusion domain that is a conspicuous feature of other partitivirus particles. ScPVs were stably maintained during laboratory growth and were successfully transferred to haploid progeny after sporulation, which provides future opportunities to study partitivirus-host interactions using the powerful genetic tools available for the model organism S. cerevisiae. Author summary: This article describes the discovery and characterization of multiple strains and species of viruses from the family Partitiviridae in the brewer’s and baker’s yeast S. cerevisiae. These novel viruses have bipartite genomes packaged in spherical viral particles with structural homology to members of the family Partitiviridae. Strikingly, yeast partitiviruses are most closely related to viruses from human pathogenic protozoa and not partitiviruses of other fungi. As partitiviruses can positively and negatively contribute to a host’s physiology (including important human and plant pathogens), the presence of partitiviruses in S. cerevisiae offers a unique opportunity to study the biology of these viruses in a well-developed model system.

A musician’s spontaneous rate of movement, called spontaneous motor tempo (SMT), can be measured while spontaneously playing a simple melody. Data shows that the SMT influences the musician’s tempo and synchronization. In this study we present a model that captures these phenomena. We review the results from three previously-published studies: solo musical performance with a pacing metronome tempo that is different from the SMT, solo musical performance without a metronome at a tempo that is faster or slower than the SMT, and duet musical performance between musicians with matching or mismatching SMTs. These studies showed, respectively, that the asynchrony between the pacing metronome and the musician’s tempo grew as a function of the difference between the metronome tempo and the musician’s SMT, musicians drifted away from the initial tempo toward the SMT, and the absolute asynchronies were smaller if musicians had matching SMTs. We hypothesize that the SMT constantly acts as a pulling force affecting musical actions at a tempo different from a musician’s SMT. To test our hypothesis, we developed a model consisting of a non-linear oscillator with Hebbian tempo learning and a pulling force to the model’s spontaneous frequency. While the model’s spontaneous frequency emulates the SMT, elastic Hebbian learning allows for frequency learning to match a stimulus’ frequency. To test our hypothesis, we first fit model parameters to match the data in the first of the three studies and asked whether this same model would explain the data the remaining two studies without further tuning. Results showed that the model’s dynamics allowed it to explain all three experiments with the same set of parameters. Our theory offers a dynamical-systems explanation of how an individual’s SMT affects synchronization in realistic music performance settings, and the model also enables predictions about performance settings not yet tested. Author summary: Individuals can keep a musical tempo on their own or timed by another individual or a metronome. Experiments show that individuals show a specific spontaneous rate of periodic action, for example walking, blinking, or singing. Moreover, in a simple metronome synchronization task, an individual’s spontaneous rate determines that the individual will tend to anticipate a metronome that is slower, and lag a metronome that is faster. Researchers have hypothesized the mechanisms explaining how spontaneous rates affect synchronization, but no hypothesis can account for all observations yet. Our hypothesis is that individuals rely on adaptive frequency learning during synchronization tasks to adapt the rate of their movements and match another individual’s actions or metronome tempo. Adaptive frequency learning also explains why an individual’s spontaneous rate persists after carrying out a musical synchronization task. We define a new model with adaptive frequency learning and use it to simulate existing empirical data. Not only can our model explain the empirical data, but it can also make testable predictions. Our results support the theory that the brain’s endogenous rhythms give rise to spontaneous rates of movement, and that learning dynamics interact with such brain rhythms to allow for flexible synchronization.

Satellites associated with plant or animal viruses have been largely detected and characterized, while those from mycoviruses together with their roles remain far less determined. Three dsRNA segments (dsRNA 1 to 3 termed according to their decreasing sizes) were identified in a strain of phytopathogenic fungus Pestalotiopsis fici AH1-1 isolated from a tea leaf. The complete sequences of dsRNAs 1 to 3, with the sizes of 10316, 5511, and 631 bp, were determined by random cloning together with a RACE protocol. Sequence analyses support that dsRNA1 is a genome of a novel hypovirus belonging to genus Alphahypovirus of the family Hypoviridae, tentatively named Pestalotiopsis fici hypovirus 1 (PfHV1); dsRNA2 is a defective RNA (D-RNA) generating from dsRNA1 with septal deletions; and dsRNA3 is the satellite component of PfHV1 since it could be co-precipitated with other dsRNA components in the same sucrose fraction by ultra-centrifuge, suggesting that it is encapsulated together with PfHV1 genomic dsRNAs. Moreover, dsRNA3 shares an identical stretch (170 bp) with dsRNAs 1 and 2 at their 5′ termini and the remaining are heterogenous, which is distinct from a typical satellite that generally has very little or no sequence similarity with helper viruses. More importantly, dsRNA3 lacks a substantial open reading frame (ORF) and a poly (A) tail, which is unlike the known satellite RNAs of hypoviruses, as well as unlike those in association with Totiviridae and Partitiviridae since the latters are encapsidated in coat proteins. As up-regulated expression of RNA3, dsRNA1 was significantly down-regulated, suggesting that dsRNA3 negatively regulates the expression of dsRNA1, whereas dsRNAs 1 to 3 have no obvious impact on the biological traits of the host fungus including morphologies and virulence. This study indicates that PfHV1 dsRNA3 is a special type of satellite-like nucleic acid that has substantial sequence homology with the host viral genome without encapsidation in a coat protein, which broadens the definition of fungal satellite. Author summary: A novel hypovirus belonging to the genus Alphahypovirus of the family Hypoviridae was identified in a phytopathogenetic P. fici isolated from a tea leaf and tentatively named Pestalotiopsis fici hypovirus 1 (PfHV1), which represents the first hypovirus from Pestalotiopsis fungi. Three RNA components (RNA 1 to 3 termed according to their decreasing sizes, dsRNA 1 to 3 termed for their replicative form are corresponding with RNA 1 to 3) associated with PfHV1 including the genomic RNA, defective RNA and satellite-like RNA (SatL-RNA) were determined, and their molecular and biological traits were characterized. The SatL-RNA negatively regulates the accumulation of PfHV1 RNA1, and represents a novel class of satellite nucleic acids since it has substantial sequence homology with the host viral genome and is not encapsidated in the coat protein of its helper virus. This study contributes to our better understanding of fungal satellites, as well as the molecular and biological traits of hypoviruses.

Genome integrity in animals depends on silencing of mobile elements by Piwi-interacting RNAs (piRNAs). A new study in this issue of PLOS Biology reveals recent evolutionary losses of key piRNA biogenesis factors in flies, highlighting adaptability by rapid shift to alternative piRNA biogenesis strategies. Genome integrity in animals depends on silencing of mobile elements by piRNAs. This Primer explores a new study in PLOS Biology that reveals recent evolutionary losses of key piRNA biogenesis factors in flies, highlighting adaptability by rapid shifts to alternative piRNA biogenesis strategies.

The chloroquine resistance transporter (PfCRT) confers resistance to a wide range of quinoline and quinoline-like antimalarial drugs in Plasmodium falciparum, with local drug histories driving its evolution and, hence, the drug transport specificities. For example, the change in prescription practice from chloroquine (CQ) to piperaquine (PPQ) in Southeast Asia has resulted in PfCRT variants that carry an additional mutation, leading to PPQ resistance and, concomitantly, to CQ re-sensitization. How this additional amino acid substitution guides such opposing changes in drug susceptibility is largely unclear. Here, we show by detailed kinetic analyses that both the CQ- and the PPQ-resistance conferring PfCRT variants can bind and transport both drugs. Surprisingly, the kinetic profiles revealed subtle yet significant differences, defining a threshold for in vivo CQ and PPQ resistance. Competition kinetics, together with docking and molecular dynamics simulations, show that the PfCRT variant from the Southeast Asian P. falciparum strain Dd2 can accept simultaneously both CQ and PPQ at distinct but allosterically interacting sites. Furthermore, combining existing mutations associated with PPQ resistance created a PfCRT isoform with unprecedented non-Michaelis-Menten kinetics and superior transport efficiency for both CQ and PPQ. Our study provides additional insights into the organization of the substrate binding cavity of PfCRT and, in addition, reveals perspectives for PfCRT variants with equal transport efficiencies for both PPQ and CQ. Author summary: Chloroquine (CQ) used to be the drug of choice against malaria until the parasite responsible for the disease became resistant. In the 1970s, piperaquine (PPQ) was introduced in areas where resistance to CQ was wide spread. In the following decade, an estimated 140 million doses were distributed, which substantially reduced the malaria burden, particularly in China, but created an environment in which PPQ resistant strains of the human malaria parasite Plasmodium falciparum emerged and spread. Interestingly, the PPQ resistant parasites displayed an increased CQ sensitivity. The main genetic determinant of both CQ and PPQ resistance in P. falciparum is a drug transporter, termed PfCRT. In this study, we used biochemical and bioinformatics approaches to understand how mutational changes in PfCRT influence the interaction of the carrier with CQ and PPQ. We found that PfCRT from CQ resistant parasites is better at transporting CQ than are PfCRT variants from PPQ resistant parasites, while the opposite is true for PPQ. We also found that PfCRT can be engineered such that it transports both antimalarials equally well. Our study offers insights into how PfCRT has evolved in response to changing drug pressure, and raises concerns regarding how it may evolve in the future.

Current mitochondrial DNA (mtDNA) haplogroup classification tools map reads to a single reference genome and perform inference based on the detected mutations to this reference. This approach biases haplogroup assignments towards the reference and prohibits accurate calculations of the uncertainty in assignment. We present HaploCart, a probabilistic mtDNA haplogroup classifier which uses a pangenomic reference graph framework together with principles of Bayesian inference. We demonstrate that our approach significantly outperforms available tools by being more robust to lower coverage or incomplete consensus sequences and producing phylogenetically-aware confidence scores that are unbiased towards any haplogroup. HaploCart is available both as a command-line tool and through a user-friendly web interface. The C++ program accepts as input consensus FASTA, FASTQ, or GAM files, and outputs a text file with the haplogroup assignments of the samples along with the level of confidence in the assignments. Our work considerably reduces the amount of data required to obtain a confident mitochondrial haplogroup assignment. Author summary: Pangenome graphs are powerful and relatively nascent data structures for representing an entire collection of genomic sequences and their homology. Here we present HaploCart, a tool which leverages the power of pangenomics, in conjunction with maximum-likelihood estimation, to improve human mtDNA haplotype inference on single-source samples (i.e. the sample is not a mixture of multiple contributors, be they human or contaminant). In this context, mapping to many reference genomes at once vastly reduces the Eurocentric bias inherent in contemporary methods, and also improves haplotyping performance at low coverage depths. We show that HaploCart is far more accurate than competing programs on simulated and empirical datasets, and reports clade-level posterior probabilities that accurately reflect confidence in our phylogenetic assignments. Our work can easily be generalized to other haploid markers and suggests that pangenome-based approaches combined with Bayesian methods show promise for improving inference and mitigating ethnicity-related bias in a large class of bioinformatics problems involving sequencing data.

Humans can easily tune in to one talker in a multitalker environment while still picking up bits of background speech; however, it remains unclear how we perceive speech that is masked and to what degree non-target speech is processed. Some models suggest that perception can be achieved through glimpses, which are spectrotemporal regions where a talker has more energy than the background. Other models, however, require the recovery of the masked regions. To clarify this issue, we directly recorded from primary and non-primary auditory cortex (AC) in neurosurgical patients as they attended to one talker in multitalker speech and trained temporal response function models to predict high-gamma neural activity from glimpsed and masked stimulus features. We found that glimpsed speech is encoded at the level of phonetic features for target and non-target talkers, with enhanced encoding of target speech in non-primary AC. In contrast, encoding of masked phonetic features was found only for the target, with a greater response latency and distinct anatomical organization compared to glimpsed phonetic features. These findings suggest separate mechanisms for encoding glimpsed and masked speech and provide neural evidence for the glimpsing model of speech perception. When humans tune in to one talker in a "cocktail party" scenario, what do we do with the non-target speech? This human intracranial study reveals new insights into the distinct mechanisms by which listeners process target and non-target speech in a crowded environment.

Neural ensembles are found throughout the brain and are believed to underlie diverse cognitive functions including memory and perception. Methods to activate ensembles precisely, reliably, and quickly are needed to further study the ensembles’ role in cognitive processes. Previous work has found that ensembles in layer 2/3 of the visual cortex (V1) exhibited pattern completion properties: ensembles containing tens of neurons were activated by stimulation of just two neurons. However, methods that identify pattern completion neurons are underdeveloped. In this study, we optimized the selection of pattern completion neurons in simulated ensembles. We developed a computational model that replicated the connectivity patterns and electrophysiological properties of layer 2/3 of mouse V1. We identified ensembles of excitatory model neurons using K-means clustering. We then stimulated pairs of neurons in identified ensembles while tracking the activity of the entire ensemble. Our analysis of ensemble activity quantified a neuron pair’s power to activate an ensemble using a novel metric called pattern completion capability (PCC) based on the mean pre-stimulation voltage across the ensemble. We found that PCC was directly correlated with multiple graph theory parameters, such as degree and closeness centrality. To improve selection of pattern completion neurons in vivo, we computed a novel latency metric that was correlated with PCC and could potentially be estimated from modern physiological recordings. Lastly, we found that stimulation of five neurons could reliably activate ensembles. These findings can help researchers identify pattern completion neurons to stimulate in vivo during behavioral studies to control ensemble activation. Author summary: Neural ensembles are groups of neurons that fire together in response to stimuli. Ensembles and pattern completion may play important functional roles in cognition. For example, in the mouse visual cortex, activation of an ensemble via stimulation of just two pattern completion neurons can drive visual perception. We improved the understanding of such pattern completion properties by developing a computational model of the visual cortex that recapitulated the cortex’s structural and functional properties. We then identified ensembles in our model, repeatedly stimulated different pairs of neurons, and recorded the fraction of times that this stimulation resulted in ensemble activation. We found that neurons that strongly activated ensembles could complete patterns when the average ensemble voltage was far from the threshold. We also found that graph theory parameters could reliably predict efficient pattern completion neurons. Lastly, we developed a novel latency metric that could identify pattern completion neurons in vivo using modern imaging techniques.

Organisms require mechanisms to distinguish self and non-self-RNA. This distinction is crucial to initiate the biogenesis of Piwi-interacting RNAs (piRNAs). In Drosophila ovaries, PIWI-guided slicing and the recognition of piRNA precursor transcripts by the DEAD-box RNA helicase Yb are the 2 known mechanisms to licence an RNA for piRNA biogenesis in the germline and the soma, respectively. Both the PIWI proteins and Yb are highly conserved across most Drosophila species and are thought to be essential to the piRNA pathway and for silencing transposons. However, we find that species closely related to Drosophila melanogaster have lost the yb gene, as well as the PIWI gene Ago3. We show that the precursor RNA is still selected in the absence of Yb to abundantly generate transposon antisense piRNAs in the soma. We further demonstrate that Drosophila eugracilis, which lacks Ago3, is completely devoid of ping-pong piRNAs and exclusively produces phased piRNAs in the absence of slicing. Thus, core piRNA pathway genes can be lost in evolution while still maintaining efficient transposon silencing. Organisms need to distinguish self and non-self RNA when initiating the biogenesis of piRNAs to target transposons. This study reveals that some Drosophila species have lost the PIWI gene Ago3 and/or the helicase RNA Yb - highly conserved components of the piRNA biogenesis pathway - yet still retain piRNA abundance and specificity.