A protocol specialized for microbial DNA extraction from living poplar wood

Microbial DNA extraction is a critical step in metagenomic research. High contents of chemical substances in wood tissues always cause low microbial DNA yield and quality. Up to date, almost no specialized methods involved in microbial DNA extraction from living wood were reported. In this study, an improved protocol (M1) concerning microbial DNA extraction from living poplar wood was developed. We compared microbial DNA yield and quality by M1 with those by other seven methods, including PowerSoil DNA isolation kit (M2), two soil microbial DNA extraction methods (M3 and M4), poplar genomic DNA extraction method from wood (M5), and microbial DNA extraction method from herb stems (M6), isolating bacteria (M7) and isolating fungus (M8). Results showed that M1 yielded much better quality and concentration of microbial DNA than the other methods (M2-M8) from both poplar wetwood and sapwood tissues. Following M1 protocol, 1 g of wetwood sample could yield 272.27 ng/ul (vol=50 ul) pure microbial DNA with the absorption ratios of 1.87 (A260/A230) and 1.66 (A260/A280). For 1 g of sapwood sample, these values were 361.83 ng/ul, 1.85 and 2.24, respectively. These DNA could be stably visualized by agarose gel electrophoresis and amplified by primer sets of bacteria (16S V3-V4, 16S-V4, 16S V4-V5) and fungus (ITS1, ITS2). While, the other seven methods only obtained less or contaminated microbial DNA, which could not be amplified stably by aforementioned primer sets. Our protocol provided an approach for microbial community study in living poplar wood in a more accurate way by molecular biology techniques

Zero-shot Preference Learning for Offline RL via Optimal Transport

Preference-based Reinforcement Learning (PbRL) has demonstrated remarkable efficacy in aligning rewards with human intentions. However, a significant challenge lies in the need of substantial human labels, which is costly and time-consuming. Additionally, the expensive preference data obtained from prior tasks is not typically reusable for subsequent task learning, leading to extensive labeling for each new task. In this paper, we propose a novel zero-shot preference-based RL algorithm that leverages labeled preference data from source tasks to infer labels for target tasks, eliminating the requirement for human queries. Our approach utilizes Gromov-Wasserstein distance to align trajectory distributions between source and target tasks. The solved optimal transport matrix serves as a correspondence between trajectories of two tasks, making it possible to identify corresponding trajectory pairs between tasks and transfer the preference labels. However, learning directly from inferred labels that contains a fraction of noisy labels will result in an inaccurate reward function, subsequently affecting policy performance. To this end, we introduce Robust Preference Transformer, which models the rewards as Gaussian distributions and incorporates reward uncertainty in addition to reward mean. The empirical results on robotic manipulation tasks of Meta-World and Robomimic show that our method has strong capabilities of transferring preferences between tasks and learns reward functions from noisy labels robustly. Furthermore, we reveal that our method attains near-oracle performance with a small proportion of scripted labels

catena-Poly[[silver(I)-μ-4,4′-bipyridine-κ2 N:N′] 4-[2-(4-carb­oxy­phen­yl)-1,1,1,3,3,3-hexa­fluoro­propan-2-yl]benzoate]

Assembly of the flexible dicarb­oxy­lic ligand 4-[2-(4-carboxyphenyl)-1,1,1,3,3,3-hexafluoropropan-2-yl]benzoate and 4,4′-bipyridine as co-ligand with AgI ions resulted in the formation of the polymeric title compound, {[Ag(C10H8N2)](C17H9F6O4)}n, in which the metal atoms are bridged by the 4,4′-bipyridine ligands, generating cationic chains extending along [010]. The dihedral angles between the benzene rings in the anion and the pyridine rings in the cation are 72.42 (9) and 9.36 (10)°, respectively. The mol­ecular conformation of the anion is stabilized by intra­molecular C—H⋯F hydrogen bonds. In the crystal, the anions inter­act with the cationic chains via C—H⋯O hydrogen bonds, forming layers parallel to (001), in which weak π–π stacking inter­actions [centroid–centroid distances = 3.975 (3)–4.047 (3) Å] involving the pyridine rings of adjacent 4,4′-bipyridine ligands are present. The planes are further assembled into a three-dimensional network by O—H⋯O hydrogen bonds

Automated Stroke Rehabilitation Assessment using Wearable Accelerometers in Free-Living Environments

Stroke is known as a major global health problem, and for stroke survivors it is key to monitor the recovery levels. However, traditional stroke rehabilitation assessment methods (such as the popular clinical assessment) can be subjective and expensive, and it is also less convenient for patients to visit clinics in a high frequency. To address this issue, in this work based on wearable sensing and machine learning techniques, we developed an automated system that can predict the assessment score in an objective and continues manner. With wrist-worn sensors, accelerometer data was collected from 59 stroke survivors in free-living environments for a duration of 8 weeks, and we aim to map the week-wise accelerometer data (3 days per week) to the assessment score by developing signal processing and predictive model pipeline. To achieve this, we proposed two new features, which can encode the rehabilitation information from both paralysed/non-paralysed sides while suppressing the high-level noises such as irrelevant daily activities. We further developed the longitudinal mixed-effects model with Gaussian process prior (LMGP), which can model the random effects caused by different subjects and time slots (during the 8 weeks). Comprehensive experiments were conducted to evaluate our system on both acute and chronic patients, and the results suggested its effectiveness.Comment: submitted to ACM Trans. Computing for Healthcar

1,4-Bis(5-methyl-1H-1,2,4-triazol-3-yl)benzene tetra­hydrate

In the title compound, C12H12N6·4H2O, the two triazole rings adopt a cis configuration with a crystallographic twofold axis passing through the central benzene group. The benzene and triazole rings are almost coplanar with a dihedral angle of 5.5 (1)°. In the crystal, water mol­ecules are joined together by OW—H⋯OW hydrogen bonds to form a one-dimensional zigzag chain. These water chains are further connected to the organic mol­ecule, forming a three-dimensional network by inter­molecular OW—H⋯N and N—H⋯OW hydrogen bonds. Moreover, π–π stacking inter­actions between triazole rings [centroid–centroid distances = 3.667 (1)–3.731 (1) Å] are observed. One of the water mol­ecules shows one of the H atoms to be disordered over two positions

A protocol specialized for microbial DNA extraction from living poplar wood

Microbial DNA extraction is a critical step in metagenomic research. High contents of chemical substances in wood tissues always cause low microbial DNA yield and quality. Up to date, almost no specialized methods involved in microbial DNA extraction from living wood were reported. In this study, an improved protocol (M1) concerning microbial DNA extraction from living poplar wood was developed. We compared microbial DNA yield and quality by M1 with those by other seven methods, including PowerSoil DNA isolation kit (M2), two soil microbial DNA extraction methods (M3 and M4), poplar genomic DNA extraction method from wood (M5), and microbial DNA extraction method from herb stems (M6), isolating bacteria (M7) and isolating fungus (M8). Results showed that M1 yielded much better quality and concentration of microbial DNA than the other methods (M2-M8) from both poplar wetwood and sapwood tissues. Following M1 protocol, 1 g of wetwood sample could yield 272.27 ng/ul (vol=50 ul) pure microbial DNA with the absorption ratios of 1.87 (A260/A230) and 1.66 (A260/A280). For 1 g of sapwood sample, these values were 361.83 ng/ul, 1.85 and 2.24, respectively. These DNA could be stably visualized by agarose gel electrophoresis and amplified by primer sets of bacteria (16S V3-V4, 16S-V4, 16S V4-V5) and fungus (ITS1, ITS2). While, the other seven methods only obtained less or contaminated microbial DNA, which could not be amplified stably by aforementioned primer sets. Our protocol provided an approach for microbial community study in living poplar wood in a more accurate way by molecular biology techniques

Identification of subtype-specific metastasis-related genetic signatures in sarcoma

Background: Sarcomas are heterogeneous rare malignancies constituting approximately 1% of all solid cancers in adults and including more than 70 histological and molecular subtypes with different pathological and clinical development characteristics. Method: We identified prognostic biomarkers of sarcomas by integrating clinical information and RNA-seq data from TCGA and GEO databases. In addition, results obtained from cell cycle, cell migration, and invasion assays were used to assess the capacity for Tanespimycin to inhibit the proliferation and metastasis of sarcoma. Results: Sarcoma samples (N = 536) were divided into four pathological subtypes including DL (dedifferentiated liposarcoma), LMS (leiomyosarcoma), UPS (undifferentiated pleomorphic sarcomas), and MFS (myxofibrosarcoma). RNA-seq expression profile data from the TCGA dataset were used to analyze differentially expressed genes (DEGs) within metastatic and non-metastatic samples of these four sarcoma pathological subtypes with DEGs defined as metastatic-related signatures (MRS). Prognostic analysis of MRS identified a group of genes significantly associated with prognosis in three pathological subtypes: DL, LMS, and UPS. ISG15, NUP50, PTTG1, SERPINE1, and TSR1 were found to be more likely associated with adverse prognosis. We also identified Tanespimycin as a drug exerting inhibitory effects on metastatic LMS subtype and therefore can serve a potential treatment for this type of sarcoma. Conclusions: These results provide new insights into the pathogenesis, diagnosis, treatment, and prognosis of sarcomas and provide new directions for further study of sarcoma

Identification and validation of aging-related genes and their classification models based on myelodysplastic syndromes

Background: Myelodysplastic syndrome is a malignant clonal disorder of hematopoietic stem cells (HSC) with both myelodysplastic problems and hematopoietic disorders. The greatest risk factor for the development of MDS is advanced age, and aging causes dysregulation and decreased function of the immune and hematopoietic systems. However, the mechanisms by which this occurs remain to be explored. Therefore, we explore the association between MDS and aging genes through a classification model and use bioinformatics analysis tools to explore the relationship between MDS aging subtypes and the immune microenvironment. Methods: The dataset of MDS in the paper was obtained from the GEO database, and aging-related genes were taken from HAGR. Specific genes were screened by three machine learning algorithms. Then, artificial neural network (ANN) models and Nomogram models were developed to validate the effectiveness of the methods. Finally, aging subtypes were established, and the correlation between MDS and the immune microenvironment was analyzed using bioinformatics analysis tools. Weighted correlation network analysis (WGCNA) and single cell analysis were also added to validate the consistency of the result analysis. Results: Seven core genes associated with ARG were screened by differential analysis, enrichment analysis and machine learning algorithms for accurate diagnosis of MDS. Subsequently, two subtypes of senescent expressions were identified based on ARG, illustrating that different subtypes have different biological and immune functions. The cell clustering results obtained from manual annotation were validated using single cell analysis, and the expression of 7 pivotal genes in MDS was verified by flow cytometry and RT-PCR. Discussion: The findings demonstrate a key role of senescence in the immunological milieu of MDS, giving new insights into MDS pathogenesis and potential treatments. The findings also show that aging plays an important function in the immunological microenvironment of MDS, giving new insights into the pathogenesis of MDS and possible immunotherapy

Poly[diaqua­bis(μ2-azido-κ2 N 1:N 1)bis­(μ3-1-oxoisonicotinato-κ3 O:O′:O′′)dicadmium(II)]

In the title compound, [Cd2(C6H4NO3)2(N3)2(H2O)2]n, one CdII atom is located on an inversion center and is coordinated by four O atoms from four bridging 1-oxoisonicotinate ligands and two N atoms of two bridging azide ligands in a slightly distorted octa­hedral geometry. The other CdII atom, also lying on an inversion center, is coordinated by four O atoms from two bridging 1-oxoisonicotinate ligands and two water mol­ecules and two N atoms of two bridging azide ligands in a slightly distorted octa­hedral geometry. The Cd atoms are connected via the 1-oxoisonicotinate and azide ligands into a two-dimensional coordination network. The crystal structure involves O—H⋯N and O—H⋯O hydrogen bonds

Isomorphic Cd(II)/Zn(II)-MOFs as Bifunctional Chemosensors for Anion (Cr2O72–) and Cation (Fe3+) detection in Aqueous Solution

Two isomorphic 3D MOFs [Cd(2-bpeb)(sdba)] (1) and [Zn(2-bpeb)(sdba)] derived from the π-conjugated pro-ligand 2-(4-((E)-2-(pyridine-2-yl)vinyl)styryl)pyridine (2-bpeb) and 4,4’-sulfonyldibenzoate (H2sdba) were synthesized and characterized. Complexes 1 and 2 exhibit striking fluorescence properties and can function as chemical sensors via rapid luminescence quenching in the presence of Fe3+and Cr2O72- in aqueous media with high sensitivity and selectivity