Two New Sexual Talaromyces Species Discovered in Estuary Soil in China

In the survey of mycobiota of mudflats in China, two new sexually reproducing Talaromyces sect. Talaromyces species were discovered and studied using a polyphasic approach. These species are named here Talaromyces haitouensis (ex-type AS3.160101T) and Talaromyces zhenhaiensis (ex-type AS3.16102T). Morphologically, T. haitouensis is distinguished by moderate growth, green-yellow gymnothecia, orange-brown mycelium, and echinulate ellipsoidal ascospores. T. zhenhaiensis is characterized by fast growth, absence of sporulation, cream yellow to naphthalene yellow gymnothecia and mycelium, and smooth-walled ellipsoidal ascospores with one equatorial ridge. The two novelties are further confirmed by phylogenetic analyses based on either individual sequences of BenA, CaM, Rpb2, and ITS1-5.8S-ITS2 or the concatenated BenA-CaM-Rpb2 sequences.

Two New Teleomorphic Talaromyces Species Discovered From The Estuary Soil In China

Two new species producing ascospores of Talaromyces sect.Talaromyces are proposed, namely, T. haitouensi (ex-type AS3.160101 T) and T. zhenhaiensis (ex-type AS3.16102 T). Morphologically, T. haitouensis is featured by moderate growth, green-yellow gymnothecia, orange-brown mycelium, and echinulate ellipsoidal ascospores. T. zhenhaiensis is characterized by fast growth, absent sporulation, cream yellow to naphthalene yellow gymnothecia and mycelium, smooth-walled ellipsoidal ascospores with one equatorial ridge. The proposal of the two new taxa is well supported by individual phylogenetic analyses based on individual sequences of BenA, CaM, Rpb2 and ITS1-5.8S-ITS2, and the concatenated BenA-CaM-Rpb2 sequence.

Quantitative Analysis of Shallow Earthquake Sequences and Regional Earthquake Behavior: Implications for Earthquake Forecasting

<p>This study is a quantitative investigation and characterization of earthquake sequences in the Central Volcanic Region (CVR) of New Zealand, and several regions in New Zealand and Southern California. We introduce CURATE, a new declustering algorithm that uses rate as the primary indicator of an earthquake sequence, and we show it has appreciable utility for analyzing seismicity. The algorithm is applied to the CVR and other regions around New Zealand. These regions are also compared with the Southern California earthquake catalogue. There is a variety of behavior within these regions, with areas that experience larger mainshock-aftershock (MS-AS) sequences having distinctly different general sequence parameters than those of more swarm dominated regions. The analysis of the declustered catalog shows that Lake Taupo and at least three other North Island regions have correlated variations in rate over periods of ~5 years. These increases in rate are not due to individual large sequences, but are instead caused by a general increase in earthquake and sequence occurrence. The most obvious increase in rate across the four North Island subsets follows the 1995-1996 magmatic eruption at Ruapehu volcano. The fact that these increases are geographically widespread and occur over years at a time suggests that the variations may reflect changes in the subduction system or a broad tectonic process. We examine basic sequence parameters of swarms and MS-AS sequences to provide better information for earthquake forecasting models. Like MS-AS sequences, swarm sequences contain a large amount of decay (decreasing rate) throughout their duration. We have tested this decay and found that 89% of MS-AS sequences and 55% of swarm sequences are better fit with an Omori's law decay than a linear rate. This result will be important to future efforts to forecast lower magnitude ranges or swarm prone areas like the CVR. To look at what types of process may drive individual sequences and may be associated with the rate changes, we examined a series of swarms that occurred to the South of Lake Taupo in 2009. We relocated these earthquakes using double-difference method, hypoDD, to obtain more accurate relative locations and depths. These swarms occur in an area about 20x20 km. They do not show systematic migration between sequences. The last swarm in the series is located in the most resistive area of the Tokaanu geothermal region and had two M =4.4 earthquakes within just four hours of each other. The earthquakes in this swarm have an accelerating rate of occurrence leading up to the first M = 4.4 earthquakes, which migrate upward in depth. The locations of earthquakes following the M = 4.4 event expand away from it at a rate consistent with fluid diffusion. Our statistical investigation of triggering due to large global (M ≥ 7) and regional earthquakes (M ≥ 6) concludes that more detailed (waveform level) investigation of individual sequences will be necessary to conclusively identify triggering, but sequence catalogs may be useful in identifying potential targets for those investigations. We also analyzed the probability that a series of swarms in the central Southern Alps were triggered by the 2009 Dusky Sound Mw = 7.8 and the 2010 Darfield Mw = 7.1 earthquake. There is less than a one-percent chance that the observed sequences occurred randomly in time. The triggered swarms do not show a significant difference to the swarms occurring in that region at other times in the 1.5-year catalog. Waveform cross-correlation was performed on this central Southern Alps earthquake catalog by a fellow PhD student Carolin Boese, and reveals that individual swarms are often composed of a single waveform family or multiple waveform families in addition to earthquakes that did not show waveform similarities. The existence of earthquakes that do not share waveform similarity in the same swarm (2.5 km radius) as a waveform family indicates that similar waveform groups may be unique in their location, but do not necessarily necessitate a unique trigger or driver. In addition to these triggered swarms in the Southern Alps we have also identified two swarms that are potentially triggered by slow-slip earthquakes along the Hikurangi margin in 2009 and 2010. The sequence catalogs generated by the CURATE method may be an ideal tool for searching for earthquake sequences triggered by slow-slip.</p>

Quantitative Analysis of Shallow Earthquake Sequences and Regional Earthquake Behavior: Implications for Earthquake Forecasting

<p>This study is a quantitative investigation and characterization of earthquake sequences in the Central Volcanic Region (CVR) of New Zealand, and several regions in New Zealand and Southern California. We introduce CURATE, a new declustering algorithm that uses rate as the primary indicator of an earthquake sequence, and we show it has appreciable utility for analyzing seismicity. The algorithm is applied to the CVR and other regions around New Zealand. These regions are also compared with the Southern California earthquake catalogue. There is a variety of behavior within these regions, with areas that experience larger mainshock-aftershock (MS-AS) sequences having distinctly different general sequence parameters than those of more swarm dominated regions. The analysis of the declustered catalog shows that Lake Taupo and at least three other North Island regions have correlated variations in rate over periods of ~5 years. These increases in rate are not due to individual large sequences, but are instead caused by a general increase in earthquake and sequence occurrence. The most obvious increase in rate across the four North Island subsets follows the 1995-1996 magmatic eruption at Ruapehu volcano. The fact that these increases are geographically widespread and occur over years at a time suggests that the variations may reflect changes in the subduction system or a broad tectonic process. We examine basic sequence parameters of swarms and MS-AS sequences to provide better information for earthquake forecasting models. Like MS-AS sequences, swarm sequences contain a large amount of decay (decreasing rate) throughout their duration. We have tested this decay and found that 89% of MS-AS sequences and 55% of swarm sequences are better fit with an Omori's law decay than a linear rate. This result will be important to future efforts to forecast lower magnitude ranges or swarm prone areas like the CVR. To look at what types of process may drive individual sequences and may be associated with the rate changes, we examined a series of swarms that occurred to the South of Lake Taupo in 2009. We relocated these earthquakes using double-difference method, hypoDD, to obtain more accurate relative locations and depths. These swarms occur in an area about 20x20 km. They do not show systematic migration between sequences. The last swarm in the series is located in the most resistive area of the Tokaanu geothermal region and had two M =4.4 earthquakes within just four hours of each other. The earthquakes in this swarm have an accelerating rate of occurrence leading up to the first M = 4.4 earthquakes, which migrate upward in depth. The locations of earthquakes following the M = 4.4 event expand away from it at a rate consistent with fluid diffusion. Our statistical investigation of triggering due to large global (M ≥ 7) and regional earthquakes (M ≥ 6) concludes that more detailed (waveform level) investigation of individual sequences will be necessary to conclusively identify triggering, but sequence catalogs may be useful in identifying potential targets for those investigations. We also analyzed the probability that a series of swarms in the central Southern Alps were triggered by the 2009 Dusky Sound Mw = 7.8 and the 2010 Darfield Mw = 7.1 earthquake. There is less than a one-percent chance that the observed sequences occurred randomly in time. The triggered swarms do not show a significant difference to the swarms occurring in that region at other times in the 1.5-year catalog. Waveform cross-correlation was performed on this central Southern Alps earthquake catalog by a fellow PhD student Carolin Boese, and reveals that individual swarms are often composed of a single waveform family or multiple waveform families in addition to earthquakes that did not show waveform similarities. The existence of earthquakes that do not share waveform similarity in the same swarm (2.5 km radius) as a waveform family indicates that similar waveform groups may be unique in their location, but do not necessarily necessitate a unique trigger or driver. In addition to these triggered swarms in the Southern Alps we have also identified two swarms that are potentially triggered by slow-slip earthquakes along the Hikurangi margin in 2009 and 2010. The sequence catalogs generated by the CURATE method may be an ideal tool for searching for earthquake sequences triggered by slow-slip.</p>

Cellular abundance shapes function in piRNA-guided genome defense

Defense against genome invaders universally relies on RNA-guided immunity. Prokaryotic CRISPR-Cas and eukaryotic RNA interference pathways recognize targets by complementary base-pairing, which places the sequences of their guide RNAs at the center of self/nonself discrimination. Here, we explore the sequence space of PIWI-interacting RNAs (piRNAs), the genome defense of animals, and establish functional priority among individual sequences. Our results reveal that only the topmost abundant piRNAs are commonly present in every cell, whereas rare sequences generate cell-to-cell diversity in flies and mice. We identify a skewed distribution of sequence abundance as a hallmark of piRNA populations and show that quantitative differences of more than a 1000-fold are established by conserved mechanisms of biogenesis. Finally, our genomics analyses and direct reporter assays reveal that abundance determines function in piRNA-guided genome defense. Taken together, we identify an effective sequence space and untangle two classes of piRNAs that differ in complexity and function. The first class represents the topmost abundant sequences and drives silencing of genomic parasites. The second class sparsely covers an enormous sequence space. These rare piRNAs cannot function in every cell, every individual, or every generation but create diversity with potential for adaptation in the ongoing arms race with genome invaders.

Measuring the Nature of Individual Sequences

This study reviews and compares indicators that can serve to characterize numerically the nature of state sequences. It also introduces several new indicators. Alongside basic measures such as the length, the number of visited distinct states, and the number of state changes, we shall consider composite measures such as turbulence and the complexity index, and measures that take account of the nature (e.g., positive vs. negative or ranking) of the states. The discussion points out the strange behavior of some of the measures—Elzinga’s turbulence and the precarity index of Ritschard, Bussi, and O’Reilly in particular—and propositions are made to avoid these flaws. The usage of the indicators is illustrated with two applications using data from the Swiss Household Panel. The first application tests the U-shape hypothesis about the evolution of life satisfaction along the life course, and the second one examines the scarring effect of earlier employment sequences.

From sequences to variables – Rethinking the relationship between sequences and outcomes

Sequence analysis (SA) has gained increasing interest in social sciences for theholistic analysis of life course and other longitudinal data. The usual approach isto construct sequences, calculate dissimilarities, group similar sequences with clusteranalysis, and use cluster membership as a dependent or independent variable in a linear or nonlinear regression model.This approach may be problematic as the cluster memberships are assumed to befixed known characteristics of the subjects in subsequent analysis. Furthermore, often it is more reasonable to assume that individual sequences are mixtures of multiple ideal types rather than equal members of some group. Failing to account for these issues may lead to wrong conclusions about the nature of the studied relationships.In this paper, we bring forward and discuss the problems of the "traditional" useof SA clusters and compare four approaches for different types of data. We conduct a simulation study and an empirical study, demonstrating the importance of considering how sequences and outcomes are related and the need to adjust the analysis accordingly. In many typical social science applications, the traditional approach is prone to result in wrong conclusions and so-called position-dependent approaches such as representativeness should be preferred.

uPIC–M: efficient and scalable preparation of clonal single mutant libraries for high-throughput protein biochemistry

New high-throughput biochemistry techniques complement selection-based approaches and provide quantitative kinetic and thermodynamic data for thousands of protein variants in parallel. With these advances, library generation rather than data collection has become rate limiting. Unlike pooled selection approaches, high-throughput biochemistry requires mutant libraries in which individual sequences are rationally designed, efficiently recovered, sequence-validated, and separated from one another, but current strategies are unable to produce these libraries at the needed scale and specificity at reasonable cost. Here, we present a scalable, rapid, and inexpensive approach for creating User-designed Physically Isolated Clonal–Mutant (uPIC–M) libraries that utilizes recent advances in oligo synthesis, high-throughput sample preparation, and next-generation sequencing. To demonstrate uPIC–M, we created a scanning mutant library of SpAP, a 541 amino acid alkaline phosphatase, and recovered 94% of desired mutants in a single iteration. uPIC–M uses commonly available equipment and freely downloadable custom software and can produce a 5000 mutant library at 1/3 the cost and 1/5 the time of traditional techniques.

Natural variants in SARS-CoV-2 Spike protein pinpoint structural and functional hotspots with implications for prophylaxis and therapeutic strategies

In December 2019, a novel coronavirus, termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified as the cause of pneumonia with severe respiratory distress and outbreaks in Wuhan, China. The rapid and global spread of SARS-CoV-2 resulted in the coronavirus 2019 (COVID-19) pandemic. Earlier during the pandemic, there were limited genetic viral variations. As millions of people became infected, multiple single amino acid substitutions emerged. Many of these substitutions have no consequences. However, some of the new variants show a greater infection rate, more severe disease, and reduced sensitivity to current prophylaxes and treatments. Of particular importance in SARS-CoV-2 transmission are mutations that occur in the Spike (S) protein, the protein on the viral outer envelope that binds to the human angiotensin-converting enzyme receptor (hACE2). Here, we conducted a comprehensive analysis of 441,168 individual virus sequences isolated from humans throughout the world. From the individual sequences, we identified 3540 unique amino acid substitutions in the S protein. Analysis of these different variants in the S protein pinpointed important functional and structural sites in the protein. This information may guide the development of effective vaccines and therapeutics to help arrest the spread of the COVID-19 pandemic.

Exploring Monte Carlo Negotiation Search with Nontrivial Agreements

The application of automated negotiations to general game playing is a research area with far-reaching implications. Non-zero sum games can be used to model a wide variety of real-world scenarios and automated negotiation provides a framework for more realistically modeling the behavior of agents in these scenarios. A particular recent development in this space is the Monte Carlo Negotiation Search (MCNS) algorithm, which can negotiate to find valuable cooperative strategies for a wide array of games (such as those of the Game Description Language). However, MCNS only proposes agreements corresponding to individual sequences of moves without any higher-level notions of conditional or stateful strategy. Our work attempts to lift this restriction. We present two contributions: extensions to the MCNS algorithm to support more complex agreements and an agreement language for GDL games suitable for use with our algorithm. We also present the results of a preliminary experiment in which we use our algorithm to search for an optimal agreement for the iterated prisoners dilemma. We demonstrate significant improvement of our algorithm over random agreement sampling, although further work is required to more consistently produce optimal agreements.