N,N′-(Propane-1,3-di­yl)bis­(p-toluene­sulfonamide)

The complete mol­ecule of the title compound, C17H22N2O4S2, is generated by crystallographic twofold symmetry, with one C atom lying on the rotation axis. The dihedral angle between the benzene rings is 44.04 (7)° and the conformation of the central N—C—C—C group is gauche. In the crystal, mol­ecules are linked by N—H⋯O hydrogen bonds, generating corrugated (010) sheets, and weak C—H⋯O inter­actions consolidate the packing

N,N′-Bis(3,3-dimethyl­all­yl)-N,N′-(prop­ane-1,3-diyl)dibenzene­sulfonamide

In the title compound, C25H34N2O4S2, the conformation of the linking N—C—C—C—N chain is gauche-anti [torsion angles = −68.49 (19) and 167.95 (14)°]. The dihedral angle between the aromatic rings is 89.64 (6)°

N,N′-Diethyl-N,N′-[1,3-phenylene­bis(methyl­ene)]dibenzene­sulfonamide

In the title compound, C24H28N2O4S2, the dihedral angles between the central benzene ring and the pendant rings are 77.44 (11) and 79.23 (10)°, and the dihedral angle between the pendant rings is 23.31 (12)°. Both sulfonamide groups project to the same side of the central benzene ring and the mol­ecule has approximate non-crystallographic mirror symmetry. One of the ethyl side chains is disordered over two sets of sites in a 0.526 (14):0.474 (14) ratio. In the crystal, inversion dimers linked by pairs of weak C—H⋯O inter­actions occur, generating R 2 2(28) loops

N,N′-Bis(3-methylbut-2-enyl)-N,N′-(1,4-phenylene)dibenzenesulfonamide

The complete mol­ecule of the title compound, C28H32N2O4S2, is generated by a crystallographic inversion centre. The dihedral angle between the central and pendant aromatic rings is 46.78 (7)°. The Car—S—N—Car (ar = aromatic) torsion angle is 73.64 (15)° and the bond-angle sum for the N atom is 350.4°. In the crystal, weak C—H⋯O inter­actions link the mol­ecules, forming a two-dimensional network lying parallel to the bc plane

N,N′-Diallyl-2,2′,5,5′-tetra­chloro-N,N′-[1,3-phenyl­enebis(methyl­ene)]dibenzene­sulfonamide

In the title compound, C26H24Cl4N2O4S2, the dihedral angles between the central benzene ring and the pendant rings are 70.07 (12) and 59.07 (12)°. The equivalent angle between the pendant rings is 79.24 (12)°. Both sulfonamide groups lie to the same side of the central ring but the pendant chains have very different conformations, as indicated by their C—S—N—C torsion angles [104.66 (17) and −76.35 (19)°] and S—N—C—C torsion angles [129.61 (17) and 147.10 (17)°]. Both N atoms are close to planar (bond angle sums = 359.0 and 354.8°). In the crystal, inversion dimers are formed via a pair of weak C—H⋯O inter­actions which generate R 2 2(22) loops

2,2′,5,5′-Tetra­chloro-N,N′-diethyl-N,N′-[benzene-1,3-diylbis(methyl­ene)]dibenzene­sulfonamide

In the title compound, C24H24Cl4N2O4S2, the dihedral angles between the central benzene ring and the pendant rings are 58.09 (10) and 62.59 (10)°. The dihedral angle between the pendant rings is 81.64 (9)°. Both sulfonamide groups lie to the same side of the central ring but the C—S—N—C torsion angles [73.09 (16) and −117.35 (14)] and S—N—C—C torsion angles [−143.80 (14) and −111.45 (16)°] differ significantly for the two pendant chains. The N atoms are close to planar (bond angle sums = 356.4 and 359.5°). In the crystal, weak C—H⋯O and C—H⋯Cl inter­actions link the mol­ecules

Monitoring health status and quality assessment of leaves using terahertz frequency

The demand for effective use of water resources has increased due to ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for precise estimation of water content (WC) in plants leaves on different days. For this purpose, multi-domain features are extracted from frequency, time, time-frequency domains using observations data to incorporate three different machine learning algorithms such as support vector machine, (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). The results demonstrate SVM outperformed other classifiers using 10-fold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for coffee, pea-shoot, and spinach leaves respectively. In addition, using SFS technique, coffee showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers. Lastly, in baby-spinach leaf, SVM exhibited an upgrade of 21.28%, 10.01%, and 8.53% was noticed in operating time for SVM, KNN and D-Tree classifiers and which eventually enhanced the classification accuracy. Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for farmers to take proactive actions in relations to plants health monitoring

Multisystem inflammatory syndrome (MIS-C) in Pakistani children: A description of the phenotypes and comparison with historical cohorts of children with Kawasaki disease and myocarditis

Objectives: To determine clinical, laboratory features and outcomes of Multisystem Inflammatory Syndrome in children (MIS-C) and its comparison with historic Kawasaki Disease (KD) and Viral Myocarditis (VM) cohorts.Methods: All children (1 month- 18 years) who fulfilled the World Health Organization criteria of MIS-C presenting to two tertiary care centers in Karachi from May 2020 till August 31st were included. KD and VM admitted to one of the study centers in the last five years prior to this pandemic, was compared to MIS-C.Results: Thirty children with median age of 24 (interquartile range (IQR)1-192) months met the criteria for MIS-C. Three phenotypes were identified, 12 patients (40%) with KD, ten (33%) VM and eight (26%) had features of TSS. Echocardiography showed coronary involvement in 10 (33%), and moderate to severe Left Ventricular dysfunction in 10 (33%) patients. Steroids and intravenous immunoglobulins (IVIG) were administered to 24 (80%) and 12 (41%) patients respectively while 7 (23%) received both. Overall, 20% children expired. During the last five years, 30 and 47 children were diagnosed with KD and VM, respectively. Their comparison with MIS-C group showed lymphopenia, thrombocytosis, and higher CRP as well as more frequent atypical presentation in MIS-C KD group with less coronary involvement. The MIS-C VM was more likely to present with fulminant myocarditis.Conclusions: Our MIS-C cohort is younger with higher mortality compared to previous reports. MIS-C is distinct from historic cohorts of KD and VM in both in clinical features and outcomes

Correction to: Transmission dynamics and control of two epidemic waves of SARS-CoV-2 in South Korea

Background After relaxing social distancing measures, South Korea experienced a resurgent second epidemic wave of coronavirus disease 2019 (COVID-19). In this study, we aimed to identify the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and assess the impact of COVID-19 case finding and contact tracing in each epidemic wave. Methods We collected data on COVID-19 cases published by local public health authorities in South Korea and divided the study into two epidemic periods (19 January–19 April 2020 for the first epidemic wave and 20 April–11 August 2020 for the second epidemic wave). To identify changes in the transmissibility of SARS-CoV-2, the daily effective reproductive number (Rt) was estimated using the illness onset of the cases. Furthermore, to identify the characteristics of each epidemic wave, frequencies of cluster types were measured, and age-specific transmission probability matrices and serial intervals were estimated. The proportion of asymptomatic cases and cases with unknown sources of infection were also estimated to assess the changes of infections identified as cases in each wave. Results In early May 2020, within 2-weeks of a relaxation in strict social distancing measures, Rt increased rapidly from 0.2 to 1.8 within a week and was around 1 until early July 2020. In both epidemic waves, the most frequent cluster types were religious-related activities and transmissions among the same age were more common. Furthermore, children were rarely infectors or infectees, and the mean serial intervals were similar (~ 3 days) in both waves. The proportion of asymptomatic cases at presentation increased from 22% (in the first wave) to 27% (in the second wave), while the cases with unknown sources of infection were similar in both waves (22 and 24%, respectively). Conclusions Our study shows that relaxing social distancing measures was associated with increased SARS-CoV-2 transmission despite rigorous case findings in South Korea. Along with social distancing measures, the enhanced contact tracing including asymptomatic cases could be an efficient approach to control further epidemic waves.This work was supported by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Korean Ministry of Education (NRF-2020R1I1A3066471). The funder of the study had no role in study design, analysis, interpretation of the data, or writing of the report