Phylogeny of the Ciliate Family Psilotrichidae (Protista, Ciliophora), a Curious and Poorly-Known Taxon, with Notes on Two Algae-Bearing Psilotrichids from Guam, USA

Background: The classification of the family Psilotrichidae, a curious group of ciliated protists with unique morphological and ontogenetic features, is ambiguous and poorly understood particularly due to the lack of molecular data. Hence, the systematic relationship between this group and other taxa in the subclass Hypotrichia remains unresolved. In this paper the morphology and phylogenetics of species from two genera of Psilotrichida are studied to shed new light on the phylogeny and species diversity of this group of ciliates. Results: The 18S rRNA gene sequences of species from two psilotrichid genera were obtained. In the phylogenetic trees, the available psilotrichid sequences are placed in a highly supported clade, justifying the establishment of the family Psilotrichidae. The morphology of two little-known species, packed with green algae, including a new species, Hemiholosticha kahli nov. spec., and Psilotrichides hawaiiensis Heber et al., 2018, is studied based on live observation, protargol impregnation, and scanning electron microscopy. Both species are easily recognized by their green coloration due to the intracellular algae, and a comprehensive discussion as to the possible roles of the intracellular algae is provided. Conclusions: The 18S rRNA gene phylogeny supports the morphological argument that Hemiholosticha, Psilotrichides and Urospinula belong to the same family, Psilotrichidae. However, the single-gene analysis, not surprisingly, does not resolve the deeper relationships of Psilotrichidae within the subclass Hypotrichia. Two littleknown psilotrichid genera with green algae were collected from the same puddle on the island of Guam, indicating a high species diversity and broader geographic distribution of this group of ciliates than previously supposed. Phylogenetic inferences from transcriptomic and/or genomic data will likely be necessary to better define the systematic position and evolution of the family Psilotrichidae. Further studies are also needed to clarify the role of the intracellular eyespot-bearing algae in these ciliates

Differentiable Genetic Programming for High-dimensional Symbolic Regression

Symbolic regression (SR) is the process of discovering hidden relationships from data with mathematical expressions, which is considered an effective way to reach interpretable machine learning (ML). Genetic programming (GP) has been the dominator in solving SR problems. However, as the scale of SR problems increases, GP often poorly demonstrates and cannot effectively address the real-world high-dimensional problems. This limitation is mainly caused by the stochastic evolutionary nature of traditional GP in constructing the trees. In this paper, we propose a differentiable approach named DGP to construct GP trees towards high-dimensional SR for the first time. Specifically, a new data structure called differentiable symbolic tree is proposed to relax the discrete structure to be continuous, thus a gradient-based optimizer can be presented for the efficient optimization. In addition, a sampling method is proposed to eliminate the discrepancy caused by the above relaxation for valid symbolic expressions. Furthermore, a diversification mechanism is introduced to promote the optimizer escaping from local optima for globally better solutions. With these designs, the proposed DGP method can efficiently search for the GP trees with higher performance, thus being capable of dealing with high-dimensional SR. To demonstrate the effectiveness of DGP, we conducted various experiments against the state of the arts based on both GP and deep neural networks. The experiment results reveal that DGP can outperform these chosen peer competitors on high-dimensional regression benchmarks with dimensions varying from tens to thousands. In addition, on the synthetic SR problems, the proposed DGP method can also achieve the best recovery rate even with different noisy levels. It is believed this work can facilitate SR being a powerful alternative to interpretable ML for a broader range of real-world problems

Tomographic Reconstruction of Rolling Contact Fatigues in Rails using 3D Eddy Current Pulsed Thermography

The detection and quantification of the rolling contact fatigue (RCF) in rail tracks are essential for rail safety and condition-based maintenance. The tomographic reconstruction of the rolling contact fatigue is challenging work. The x-ray is unable to do in-situ inspection effectively. This paper proposes a new approach for RCF construction using 3D eddy current pulsed thermography. A differential time-square-root (sqrt) of temperature drop (DTSTD) is proposed as a mean to construct the sectional images and to reconstruct the thermal tomography image. The proposed method is validated through artificial angular crack slots as well as natural RCF crack. The thermal tomographic reconstruction is compared with the x-ray computed tomography on a rail track head cut-off with RCF cracks

Response of the phytoplankton community to water quality in a local alpine glacial lake of Xinjiang Tianchi, China: potential drivers and management implications

Eutrophication has become one of the most serious threats to aquatic ecosystems in the world. With the combined drivers of climate change and human activities, eutrophication has expanded from warm shallow lakes to cold-water lakes in relatively high latitude regions and has raised greater concerns over lake aquatic ecosystem health. A two-year field study was carried out to investigate water quality, phytoplankton characteristics and eutrophication status in a typical alpine glacial lake of Tianchi, a scenic area and an important drinking water source in the Xinjiang Autonomous Region of China, in 2014 and 2015. Clear seasonal and annual variations of nutrients and organic pollutants were found especially during rainy seasons. For the phytoplankton community, Bacillariophyta held the dominant position in terms of both species and biomass throughout the year, suggesting the dominant characteristics of diatoms in the phytoplankton structure in such a high-altitude cold-water lake. This was quite different from plain and warm lakes troubled with cyanobacterial blooming. Moreover, the dominant abundance of Cyclotella sp. in Tianchi might suggest regional warming caused by climate change, which might have profound effects on the local ecosystems and hydrological cycle. Based on water quality parameters, a comprehensive trophic level index TLI (Σ) was calculated to estimate the current status of eutrophication, and the results inferred emerging eutrophication in Tianchi. Results from Canonical Correspondence Analysis (CCA) and correlation analysis of phytoplankton genera and physico-chemical variables of water indicated that abiotic factors significantly influenced the phytoplankton community and its succession in Tianchi Lake. These abiotic factors could explain 77.82% of the total variance, and ammonium was identified as the most discriminant variable, which could explain 41% of the total variance followed by TP (29%). An estimation of annual nutrient loadings to Tianchi was made, and the results indicated that about 212.97 t of total nitrogen and 32.14 t of total phosphorus were transported into Tianchi Lake annually. Human socio-economic activities (runoff caused by historical overgrazing and increasing tourism) were identified as the most important contributors to Tianchi nutrient loadings