Hydrogen-bond landscapes, geometry and energetics of squaric acid and its mono- and dianions: a Cambridge Structural Database, IsoStar and computational study

As part of a programme of work to extend central-group coverage in the Cambridge Crystallographic Data Centre's (CCDC) IsoStar knowledge base of intermolecular interactions, we have studied the hydrogen-bonding abilities of squaric acid (H(2)SQ) and its mono-and dianions (HSQ(-) and SQ(2-)) using the Cambridge Structural Database (CSD) along with dispersion-corrected density functional theory (DFT-D) calculations for a range of hydrogen-bonded dimers. The -OH and -C=O groups of H(2)SQ, HSQ(-) and SQ(2-) are potent donors and acceptors, as indicated by their hydrogenbond geometries in available crystal structures in the CSD, and by the attractive energies calculated for their dimers with acetone and methanol, which were used as model acceptors and donors. The two anions have sufficient examples in the CSD for their addition as new central groups in IsoStar. It is also shown that charge-and resonance-assisted hydrogen bonds involving H(2)SQ and HSQ(-) are similar in strength to those made by carboxylate COO- acceptors, while hydrogen bonds made by the dianion SQ(2-) are somewhat stronger. The study reinforces the value of squaric acid and its anions as cocrystal formers and their actual and potential importance as isosteric replacements for carboxylic acid and carboxylate functions

"Delirium Day": A nationwide point prevalence study of delirium in older hospitalized patients using an easy standardized diagnostic tool

Background: To date, delirium prevalence in adult acute hospital populations has been estimated generally from pooled findings of single-center studies and/or among specific patient populations. Furthermore, the number of participants in these studies has not exceeded a few hundred. To overcome these limitations, we have determined, in a multicenter study, the prevalence of delirium over a single day among a large population of patients admitted to acute and rehabilitation hospital wards in Italy. Methods: This is a point prevalence study (called "Delirium Day") including 1867 older patients (aged 65 years or more) across 108 acute and 12 rehabilitation wards in Italian hospitals. Delirium was assessed on the same day in all patients using the 4AT, a validated and briefly administered tool which does not require training. We also collected data regarding motoric subtypes of delirium, functional and nutritional status, dementia, comorbidity, medications, feeding tubes, peripheral venous and urinary catheters, and physical restraints. Results: The mean sample age was 82.0 \ub1 7.5 years (58 % female). Overall, 429 patients (22.9 %) had delirium. Hypoactive was the commonest subtype (132/344 patients, 38.5 %), followed by mixed, hyperactive, and nonmotoric delirium. The prevalence was highest in Neurology (28.5 %) and Geriatrics (24.7 %), lowest in Rehabilitation (14.0 %), and intermediate in Orthopedic (20.6 %) and Internal Medicine wards (21.4 %). In a multivariable logistic regression, age (odds ratio [OR] 1.03, 95 % confidence interval [CI] 1.01-1.05), Activities of Daily Living dependence (OR 1.19, 95 % CI 1.12-1.27), dementia (OR 3.25, 95 % CI 2.41-4.38), malnutrition (OR 2.01, 95 % CI 1.29-3.14), and use of antipsychotics (OR 2.03, 95 % CI 1.45-2.82), feeding tubes (OR 2.51, 95 % CI 1.11-5.66), peripheral venous catheters (OR 1.41, 95 % CI 1.06-1.87), urinary catheters (OR 1.73, 95 % CI 1.30-2.29), and physical restraints (OR 1.84, 95 % CI 1.40-2.40) were associated with delirium. Admission to Neurology wards was also associated with delirium (OR 2.00, 95 % CI 1.29-3.14), while admission to other settings was not. Conclusions: Delirium occurred in more than one out of five patients in acute and rehabilitation hospital wards. Prevalence was highest in Neurology and lowest in Rehabilitation divisions. The "Delirium Day" project might become a useful method to assess delirium across hospital settings and a benchmarking platform for future surveys

Fluorine-substituted cyclobutenes in the solid state: Crystal structures, vibrational spectra and mechanical and thermodynamic properties

23 pags., 15 figs., 16 tabs.The crystal structures, infrared and Raman spectra, and mechanical and thermodynamic properties of four important fluorine-substituted cyclobutene derivatives in the solid state are investigated using first-principles solid-state methods based on density functional theory. These compounds are hexafluorocyclobutene (HFCB, CF), 1,3,3,4,4-pentafluoro-2-methoxycyclobut-1-ene (PFMCB, CFOCH), 3,3,4,4-tetrafluoro-1,2-dimethoxycyclobut-1-ene [TFDMCB, CF(OCH)] and 1,2-dichloro-3,3,4,4-tetrafluorocyclobut-1-ene (DCTFCB, CClF). Although some of the properties of the corresponding molecules in the gas phase have been studied, and the structures of the corresponding molecular crystals have been determined by refinement from X-ray diffraction data, their vibrational spectra and properties have not yet been reported. The computed crystal structures and X-ray diffraction patterns are in excellent agreement with their experimental counterparts. The infrared and Raman spectra are calculated from the computed crystal structures using density functional perturbation theory. The results are highly consistent with the corresponding spectra measured experimentally in the gas or liquid phases and, therefore, appropriate normal coordinate analyses of the theoretical results are employed to rigorously assign all the vibrational bands. The elasticity matrices of these materials are computed using the finite deformation technique and a complete set of relevant mechanical properties is determined. Their equations of state are also obtained. These compounds are shown to be weak, highly anisotropic materials displaying the negative Poisson's ratio phenomenon. The TFDMCB also exhibits the negative linear compressibility (NLC) phenomenon for external isotropic pressures in the range of 0.64–1.76 GPa. The computed minimum compressibility, found at P = 0.73 GPa, is substantial (k=−192.9 TPa). The NLC effect in TFDMCB is due to a collective rotation of the molecules within the crystal under increasing pressure. Finally, the thermodynamic properties of these materials are determined as a function of temperature using phonon calculations. The computed specific heats of HFCB, PFMCB, TFDMCB and DCMCB at T = 250 K are 127.5, 150.4, 169.9 and 134.8 J·K·mol, respectively, and corresponding entropies are 152.8, 173.3, 195.4 and 186.6 J·K·mol.The supercomputer time provided by the CTI-CSIC center is grate- fully acknowledge

Setup for Semimicro Pressure Filtration

Semimicro pressure filtration is carried out by pressurizing a suspension through pushing the piston of a gas-filled plastic syringe whose needle perforates the closure of a cylindrical filter funnel. Basic features as well as pros and cons of vacuum and pressure filtration are pointed out and compared to support rational choice between the two procedures

Analysis of Deep Learning Neural Networks for Seismic Impedance Inversion: A Benchmark Study

Neural networks have been applied to seismic inversion problems since the 1990s. More recently, many publications have reported the use of Deep Learning (DL) neural networks capable of performing seismic inversion with promising results. However, when solving a seismic inversion problem with DL, each author uses, in addition to different DL models, different datasets and different metrics for performance evaluation, which makes it difficult to compare performances. Depending on the data used for training and the metrics used for evaluation, one model may be better or worse than another. Thus, it is quite challenging to choose the appropriate model to meet the requirements of a new problem. This work aims to review some of the proposed DL methodologies, propose appropriate performance evaluation metrics, compare the performances, and observe the advantages and disadvantages of each model implementation when applied to the chosen datasets. The publication of this benchmark environment will allow fair and uniform evaluations of newly proposed models and comparisons with currently available implementations

Analysis of Deep Learning Neural Networks for Seismic Impedance Inversion: A Benchmark Study

Neural networks have been applied to seismic inversion problems since the 1990s. More recently, many publications have reported the use of Deep Learning (DL) neural networks capable of performing seismic inversion with promising results. However, when solving a seismic inversion problem with DL, each author uses, in addition to different DL models, different datasets and different metrics for performance evaluation, which makes it difficult to compare performances. Depending on the data used for training and the metrics used for evaluation, one model may be better or worse than another. Thus, it is quite challenging to choose the appropriate model to meet the requirements of a new problem. This work aims to review some of the proposed DL methodologies, propose appropriate performance evaluation metrics, compare the performances, and observe the advantages and disadvantages of each model implementation when applied to the chosen datasets. The publication of this benchmark environment will allow fair and uniform evaluations of newly proposed models and comparisons with currently available implementations