Inorganic–Organic Modified Bentonite as a Functional Sorbent for Cd2+ and Phenol

Functional sorbent Al-DTPA-CTMAB-bentonite (hydroxyl aluminum-diethylene triamine pentaacetic acid-hexadecyl trimethyl ammonium bromide-bentonite) was synthesized by placing hydroxyl-aluminum pillared agent, alkylammonium cation and organic chelating agents onto bentonite. The simultaneous adsorption of organic pollutant (phenol) and heavy metals (Cd2+) mixed contaminant on Al-DTPA-CTMAB-bentonite was investigated. The Al-DTPA-CTMAB-bentonite showed significant adsorption for the mixed contaminant from aqueouss solution. The Langmuir and Freundlich isotherm equations were applied to the data and values of parameters of these isotherm equations were evaluated

Individual participant data meta-analysis to compare EPDS accuracy to detect major depression with and without the self-harm item

Item 10 of the Edinburgh Postnatal Depression Scale (EPDS) is intended to assess thoughts of intentional self-harm but may also elicit concerns about accidental self-harm. It does not specifically address suicide ideation but, nonetheless, is sometimes used as an indicator of suicidality. The 9-item version of the EPDS (EPDS-9), which omits item 10, is sometimes used in research due to concern about positive endorsements of item 10 and necessary follow-up. We assessed the equivalence of total score correlations and screening accuracy to detect major depression using the EPDS-9 versus full EPDS among pregnant and postpartum women. We searched Medline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, and Web of Science from database inception to October 3, 2018 for studies that administered the EPDS and conducted diagnostic classification for major depression based on a validated semi-structured or fully structured interview among women aged 18 or older during pregnancy or within 12 months of giving birth. We conducted an individual participant data meta-analysis. We calculated Pearson correlations with 95% prediction interval (PI) between EPDS-9 and full EPDS total scores using a random effects model. Bivariate random-effects models were fitted to assess screening accuracy. Equivalence tests were done by comparing the confidence intervals (CIs) around the pooled sensitivity and specificity differences to the equivalence margin of δ = 0.05. Individual participant data were obtained from 41 eligible studies (10,906 participants, 1407 major depression cases). The correlation between EPDS-9 and full EPDS scores was 0.998 (95% PI 0.991, 0.999). For sensitivity, the EPDS-9 and full EPDS were equivalent for cut-offs 7–12 (difference range − 0.02, 0.01) and the equivalence was indeterminate for cut-offs 13–15 (all differences − 0.04). For specificity, the EPDS-9 and full EPDS were equivalent for all cut-offs (difference range 0.00, 0.01). The EPDS-9 performs similarly to the full EPDS and can be used when there are concerns about the implications of administering EPDS item 10. Trial registration: The original IPDMA was registered in PROSPERO (CRD42015024785)

Locally Adaptive Algorithms for Multiple Testing with Network Structure, with Application to Genome-Wide Association Studies

Linkage analysis has provided valuable insights to the GWAS studies, particularly in revealing that SNPs in linkage disequilibrium (LD) can jointly influence disease phenotypes. However, the potential of LD network data has often been overlooked or underutilized in the literature. In this paper, we propose a locally adaptive structure learning algorithm (LASLA) that provides a principled and generic framework for incorporating network data or multiple samples of auxiliary data from related source domains; possibly in different dimensions/structures and from diverse populations. LASLA employs a $p$-value weighting approach, utilizing structural insights to assign data-driven weights to individual test points. Theoretical analysis shows that LASLA can asymptotically control FDR with independent or weakly dependent primary statistics, and achieve higher power when the network data is informative. Efficiency again of LASLA is illustrated through various synthetic experiments and an application to T2D-associated SNP identification.Comment: 33 pages, 7 figure

Proteomic Analysis of Bacillus thuringiensis Strain 4.0718 at Different Growth Phases

The growth process of Bacillus thuringiensis Bt4.0718 strain was studied using proteomic technologies. The proteins of Bt whole cells at three phases—middle vegetative, early sporulation, and late sporulation—were extracted with lysis buffer, followed with separation by 2-DE and identified by MALDI-TOF/TOF MS. Bioactive factors such as insecticidal crystal proteins (ICPs) including Cry1Ac(3), Cry2Aa, and BTRX28, immune inhibitor (InhA), and InhA precursor were identified. InhA started to express at the middle vegetative phase, suggesting its contribution to the survival of Bt in the host body. At the early sporulation phase, ICPs started their expression. CotJC, OppA, ORF1, and SpoIVA related to the formation of crystals and spores were identified, the expression characteristics of which ensured the stable formation of crystals and spores. This study provides an important foundation for further exploration of the stable expression of ICPs, the smooth formation of crystals, and the construction of recombinant strains

Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution

The ongoing challenges in time series anomaly detection (TSAD), notably the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more efficient solution. As limited anomaly labels hinder traditional supervised models in TSAD, various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of having explicit anomalies that even random functions can detect. This problem is exacerbated by ill-posed evaluation metrics, known as point adjustment (PA), which can result in inflated model performance. In this context, we propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD), which addresses these challenges by modeling features across three data domains - temporal, frequency, and residual domains - without relying on anomaly labels. Unlike traditional contrastive learning methods, TriAD employs both inter-domain and intra-domain contrastive loss to learn common attributes among normal data and differentiate them from anomalies. Additionally, our approach can detect anomalies of varying lengths by integrating with a discord discovery algorithm. It is worth noting that this study is the first to reevaluate the deep learning potential in TSAD, utilizing both rigorously designed datasets (i.e., UCR Archive) and evaluation metrics (i.e., PA%K and affiliation). Through experimental results on the UCR dataset, TriAD achieves an impressive three-fold increase in PA%K based F1 scores over SOTA deep learning models, and 50% increase of accuracy as compared to SOTA discord discovery algorithms.Comment: This work is submitted to IEEE International Conference on Data Engineering (ICDE) 202

4-[1-(Hydroxy­imino)ethyl]-N-(4-nitro­benzyl­idene)aniline

In the title compound, C15H13N3O3, the dihedral angle formed by the two benzene rings is 44.23 (2)°. The crystal structure is stabilized by aromatic π–π stacking inter­actions, with centroid-centroid distances of 3.825 (3) and 3.870 (4) Å between the aniline and the nitro­benzene rings of neighbouring mol­ecules, respectively. In addition, the stacked mol­ecules exhibit inter­molecular C—H⋯N and C—H⋯O inter­actions

1,3-Bis[(4-nitro­benzyl­idene)amino­oxy]propane

The complete molecule of title compound, C17H16N4O6, is generated by a crystallographic twofold axis. Within the mol­ecule, the two benzene units are approximately perpen­dicular, making a dihedral angle of 85.91 (4)°. In the crystal, mol­ecules are linked into a three-dimensional network by C—H⋯O hydrogen bonds and short O⋯O and N⋯O inter­actions, with distances of 2.998 (2) and 2.968 (3) Å, respectively

(−)-Dimethyl 3,3′-diphenyl-2,2′-[pyridine-2,6-diylbis(carbonyl­imino)]dipropanoate

The title compound, C27H27N3O6, a bis-amide derivative, is also a chiral amino acid ester with l-phenyl­alanine methyl ester groups as amine substituents. The pyridine ring is oriented at dihedral angles of 89.69 (3) and 62.95 (3)° with respect to the phenyl rings, while the dihedral angle between the phenyl rings is 60.76 (3)°. In the crystal structure, inter­molecular N—H⋯O hydrogen bonds link the mol­ecules into chains. One of the carbonyl O atoms and one of the meth­oxy CH3 groups are disordered over two positions. The O atom was refined with occupancies of 0.69 (13) and 0.31 (13), while C and H atoms were refined with occupancies of 0.69 (8) and 0.31 (8)

6,6′-Dieth­oxy-2,2′-[propane-1,3-diyl­dioxy­bis(nitrilo­methyl­idyne)]diphenol

The complete mol­ecule of the title compound, C21H26N2O6, is generated by a crystallographic twofold axis and adopts a trans configuration with respect to the azomethine group. The two benzene rings are almost perpendicular to one another, making a dihedral angle of 89.53 (3)°. In the mol­ecular structure, pairs of intra­molecular O—H⋯N hydrogen bonds generate two six-membered rings. The crystal structure is further stabilized by inter­molecular C—H⋯O hydrogen bonds, which link four adjacent mol­ecules into a network structure