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Gravity-Driven Multiple Effect Thermal System (G-METS) distillation for metals purification 

As the continuation of Fungal Diversity Notes series, the current paper is the 16th contribution to this series. A total of 103 taxa from seven classes in Ascomycota and Basidiomycota are included here. Of these 101 taxa, four new genera, 89 new species, one new combination, one new name and six new records are described in detail along with information of hosts and geographic distributions. The four genera newly introduced are Ascoglobospora, Atheliella, Rufoboletus and Tenuimyces. Newly described species are Akanthomyces xixiuensis, Agaricus agharkarii, A. albostipitatus, Amphisphaeria guttulata, Ascoglobospora marina, Astrothelium peudostraminicolor, Athelia naviculispora, Atheliella conifericola, Athelopsis subglaucina, Aureoboletus minimus, A. nanlingensis, Autophagomyces incertus, Beltrania liliiferae, Beltraniella jiangxiensis, Botryobasidium coniferarum, Calocybella sribuabanensis, Calonarius caesiofulvus, C. nobilis, C. pacificus, C. pulcher, C. subcorrosus, Cortinarius flaureifolius, C. floridaensis, C. subiodes, Crustomyces juniperi, C. scytinostromoides, Cystostereum subsirmaurense, Dimorphomyces seemanii, Fulvoderma microporum, Ginnsia laricicola, Gomphus zamorinorum, Halobyssothecium sichuanense, Hemileccinum duriusculum, Henningsomyces hengduanensis, Hygronarius californicus, Kneiffiella pseudoabdita, K. pseudoalutacea, Laboulbenia bifida, L. tschirnhausii, L. tuberculata, Lambertella dipterocarpacearum, Laxitextum subrubrum, Lyomyces austro-occidentalis, L. crystallina, L. guttulatus, L. niveus, L. tasmanicus, Marasmius centrocinnamomeus, M. ferrugineodiscus, Megasporoporia tamilnaduensis, Meruliopsis crystallina, Metuloidea imbricata, Moniliophthora atlantica, Mystinarius ochrobrunneus, Neomycoleptodiscus alishanense, Nigrograna kunmingensis, Paracremonium aquaticum, Parahelicomyces dictyosporus, Peniophorella sidera, P. subreticulata, Phlegmacium fennicum, P. pallidocaeruleum, Pholiota betulicola, P. subcaespitosa, Pleurotheciella hyalospora, Pleurothecium aseptatum, Resupinatus porrigens, Russula chlorina, R. chrysea, R. cruenta, R. haematina, R. luteocarpa, R. sanguinolenta, Synnemellisia punensis, Tenuimyces bambusicola, Thaxterogaster americanoporphyropus, T. obscurovibratilis, Thermoascus endophyticus, Trechispora alba, T. perminispora, T. subfarinacea, T. tuberculata, Tremella sairandhriana, Tropicoporus natarajaniae, T. subramaniae, Usnea kriegeriana, Wolfiporiella macrospora and Xylodon muchuanensis. Rufoboletus hainanensis is newly transferred from Butyriboletus, while a new name Russula albocarpa is proposed for Russula leucocarpa G.J. Li & Chun Y. Deng an illegitimate later homonym of Russula leucocarpa (T. Lebel) T. Lebel. The new geographic distribution regions are recorded for Agaricus bambusetorum, Bipolaris heliconiae, Crinipellis trichialis, Leucocoprinus cretaceus, Halobyssothecium cangshanense and Parasola setulosa. Corresponding to morphological characters, phylogenetic evidence is also utilized to place the above-mentioned taxa in appropriate taxonomic positions. The current morphological and phylogenetic data is helpful for further clarification of species diversity and exploration of evolutionary relationships in the related fungal groups. 

Topology optimization algorithms often employ a smooth density function to implicitly represent geometries in a discretized domain. While this implicit representation offers great flexibility to parametrize the optimized geometry, it also leads to a transition region. Previous approaches, such as the Solid Isotropic Material Penalty (SIMP) method, have been proposed to modify the objective function aiming to converge toward integer density values and eliminate this non-physical transition region. However, the iterative nature of topology optimization renders this process computationally demanding, emphasizing the importance of achieving fast convergence. Accelerating convergence without significantly compromising the final solution can be challenging. In this work, we introduce a machine learning approach that leverages the message-passing Graph Neural Network (GNN) to eliminate the non-physical transition zone for the topology optimization problems. By representing the optimized structures as weighted graphs, we introduce a generalized filtering algorithm based on the topology of the spatial discretization. As such, the resultant algorithm can be applied to two- and three-dimensional space for both Cartesian (structured grid) and non-Cartesian discretizations (e.g. polygon finite element). The numerical experiments indicate that applying this filter throughout the optimization process may avoid excessive iterations and enable a more efficient optimization procedure. 

A random sampling-and-averaging (RSA) technique based on stochastic Monte Carlo methods is described in this paper for enhancing the accuracy of single-photon arrival-time measurements down to sub-picosecond ranges in emerging quantum applications. The theoretical variances of both synchronous and asynchronous RSA techniques are presented in the mathematical formats and experimentally verified by the Monte Carlo simulations. Meanwhile, the methodology of converting the mathematical models into an almost all-digital low-power integrated-circuit is elaborated by a circuit-level example with the instruction of setting circuit parameters. Along with the superior measurement resolution, scalable dynamic ranges, high linearity, high noise immunity, and low power/area consumption, the primary limitation of the RSA techniques has also been addressed for the forthcoming conversion-rate enhancement techniques. 

ABSTRACT Complex interactions exist among microorganisms in a community to carry out ecological processes and adapt to changing environments. Here, we constructed a quad-culture consisting of a cellulolytic bacterium ( Ruminiclostridium cellulolyticum ), a hydrogenotrophic methanogen ( Methanospirillum hungatei ), an acetoclastic methanogen ( Methanosaeta concilii ), and a sulfate-reducing bacterium ( Desulfovibrio vulgaris ). The four microorganisms in the quad-culture cooperated via cross-feeding to produce methane using cellulose as the only carbon source and electron donor. The community metabolism of the quad-culture was compared with those of the R. cellulolyticum -containing tri-cultures, bi-cultures, and mono-culture. Methane production was higher in the quad-culture than the sum of the increases in the tri-cultures, which was attributed to a positive synergy of four species. In contrast, cellulose degradation by the quad-culture was lower than the additive effects of the tri-cultures which represented a negative synergy. The community metabolism of the quad-culture was compared between a control condition and a treatment condition with sulfate addition using metaproteomics and metabolic profiling. Sulfate addition enhanced sulfate reduction and decreased methane and CO 2 productions. The cross-feeding fluxes in the quad-culture in the two conditions were modeled using a community stoichiometric model. Sulfate addition strengthened metabolic handoffs from R. cellulolyticum to M. concilii and D. vulgaris and intensified substrate competition between M. hungatei and D. vulgaris . Overall, this study uncovered emergent properties of higher-order microbial interactions using a four-species synthetic community. IMPORTANCE A synthetic community was designed using four microbial species that together performed distinct key metabolic processes in the anaerobic degradation of cellulose to methane and CO 2 . The microorganisms exhibited expected interactions, such as cross-feeding of acetate from a cellulolytic bacterium to an acetoclastic methanogen and competition of H 2 between a sulfate reducing bacterium and a hydrogenotrophic methanogen. This validated our rational design of the interactions between microorganisms based on their metabolic roles. More interestingly, we also found positive and negative synergies as emergent properties of high-order microbial interactions among three or more microorganisms in cocultures. These microbial interactions can be quantitatively measured by adding and removing specific members. A community stoichiometric model was constructed to represent the fluxes in the community metabolic network. This study paved the way toward a more predictive understanding of the impact of environmental perturbations on microbial interactions sustaining geochemically significant processes in natural systems.