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1. Introducing the Self‐Cleaning FiLtrAtion for Water quaLity SenSors ( SC‐FLAWLeSS ) system

   https://doi.org/10.1002/lom3.10377  

   Khandelwal, Aashish ; González‐Pinzón, Ricardo ; Regier, Peter ; Nichols, Justin ; Van Horn, David J. ( July 2020 , Limnology and Oceanography: Methods)

   Sensor‐based, semicontinuous observations of water quality parameters have become critical to understanding how changes in land use, management, and rainfall‐runoff processes impact water quality at diurnal to multidecadal scales. While some commercially available water quality sensors function adequately under a range of turbidity conditions, other instruments, including those used to measure nutrient concentrations, cease to function in high turbidity waters (> 100 nephelometric turbidity units [NTU]) commonly found in large rivers, arid‐land rivers, and coastal areas. This is particularly true during storm events, when increases in turbidity are often concurrent with increases in nutrient transport. Here, we present the development and validation of a system that can affordably provide Self‐Cleaning FiLtrAtion for Water quaLity SenSors (SC‐FLAWLeSS), and enables long‐term, semicontinuous data collection in highly turbid waters. The SC‐FLAWLeSS system features a three‐step filtration process where: (1) a coarse screen at the inlet removes particles with diameter > 397 μm, (2) a settling tank precipitates and then removes particles with diameters between 10 and 397 μm, and (3) a self‐cleaning, low‐cost, hollow fiber membrane technology removes particles ≥ 0.2μm. We tested the SC‐FLAWLeSS system by measuring nitrate sensor data loss during controlled, serial sediment additions in the laboratory and validated it by monitoring soluble phosphate concentrations in the arid Rio Grande river (New Mexico, U.S.A.), at hourly sampling resolution. Our data demonstrate that the system can resolve turbidity‐related interference issues faced by in situ optical and wet chemistry sensors, even at turbidity levels > 10,000 NTU.

    

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2. Integrating deep learning, threading alignments, and a multi‐MSA strategy for high‐quality protein monomer and complex structure prediction in CASP15

   https://doi.org/10.1002/prot.26585  

   Zheng, Wei ; Wuyun, Qiqige ; Freddolino, Peter L. ; Zhang, Yang ( August 2023 , Proteins: Structure, Function, and Bioinformatics)

   We report the results of the “UM‐TBM” and “Zheng” groups in CASP15 for protein monomer and complex structure prediction. These prediction sets were obtained using the D‐I‐TASSER and DMFold‐Multimer algorithms, respectively. For monomer structure prediction, D‐I‐TASSER introduced four new features during CASP15: (i) a multiple sequence alignment (MSA) generation protocol that combines multi‐source MSA searching and a structural modeling‐based MSA ranker; (ii) attention‐network based spatial restraints; (iii) a multi‐domain module containing domain partition and arrangement for domain‐level templates and spatial restraints; (iv) an optimized I‐TASSER‐based folding simulation system for full‐length model creation guided by a combination of deep learning restraints, threading alignments, and knowledge‐based potentials. For 47 free modeling targets in CASP15, the final models predicted by D‐I‐TASSER showed average TM‐score 19% higher than the standard AlphaFold2 program. We thus showed that traditional Monte Carlo‐based folding simulations, when appropriately coupled with deep learning algorithms, can generate models with improved accuracy over end‐to‐end deep learning methods alone. For protein complex structure prediction, DMFold‐Multimer generated models by integrating a new MSA generation algorithm (DeepMSA2) with the end‐to‐end modeling module from AlphaFold2‐Multimer. For the 38 complex targets, DMFold‐Multimer generated models with an average TM‐score of 0.83 and Interface Contact Score of 0.60, both significantly higher than those of competing complex prediction tools. Our analyses on complexes highlighted the critical role played by MSA generating, ranking, and pairing in protein complex structure prediction. We also discuss future room for improvement in the areas of viral protein modeling and complex model ranking.

    

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3. rrQNet : Protein contact map quality estimation by deep evolutionary reconciliation

   https://doi.org/10.1002/prot.26394  

   Roche, Rahmatullah ; Bhattacharya, Sutanu ; Shuvo, Md. Hossain ; Bhattacharya, Debswapna ( June 2022 , Proteins: Structure, Function, and Bioinformatics)
4. Hydrologic connectivity and land cover affect floodplain lake water quality, fish abundance, and fish diversity in floodplain lakes of the Wabash‐White River basin

   https://doi.org/10.1002/rra.3888  

   Carlson Mazur, Martha L. ; Smith, Bradley ; Bird, Broxton ; McMillan, Sara ; Pyron, Mark ; Hauswald, Cassie ( January 2022 , River Research and Applications)
5. Quality of life independently predicts overall survival in myelofibrosis: Key insights from the COntrolled MyeloFibrosis Study with ORal Janus kinase inhibitor Treatment (COMFORT)‐I study

   https://doi.org/10.1111/bjh.18329  

   Kosiorek, Heidi E. ; Scherber, Robyn M. ; Geyer, Holly L. ; Verstovsek, Srdan ; Langlais, Blake T. ; Mazza, Gina L. ; Gotlib, Jason ; Gupta, Vikas ; Padrnos, Leslie J. ; Palmer, Jeanne M. ; et al ( September 2022 , British Journal of Haematology)