The Electoral Misinformation Nexus: How News Consumption, Platform Use, and Trust in News Influence Belief in Electoral Misinformation.

Electoral misinformation, where citizens believe false or misleading claims about the electoral process and electoral institutions-sometimes actively and strategically spread by political actors-is a challenge to public confidence in elections specifically and democracy more broadly. In this article, we analyze a combination of 42 million clicks in links and apps from behavioral tracking data of 2,200 internet users and a four-wave panel survey to investigate how different kinds of online news and media use relate to beliefs in electoral misinformation during a contentious political period-the 2022 Brazilian presidential elections. We find that, controlling for other factors, using news from legacy news media is associated with belief in fewer claims of electoral misinformation over time. We find null or inconsistent effects for using digital-born news media and various digital platforms, including Facebook and WhatsApp. Furthermore, we find that trust in news plays a significant role as a moderator. Belief in electoral misinformation, in turn, undermines trust in news. Overall, our findings document the important role of the news media as an institution in curbing electoral misinformation, even as they also underline the precarity of trust in news during contentious political periods.

Psychological Assessment of Mothers of Indian Children with Differences of Sex Development.

OBJECTIVE: To assess the parental stress, coping mechanism and quality of life of caregivers of children with Differences of Sex Development (DSD). METHODS: Mothers of children (6 months - 12 years) with DSD were enrolled after excluding mothers of children with syndromic diagnosis, developmental delay, cognitive impairments, chronic diseases, or if the duration of DSD diagnosis was less than six months, and mothers with psychiatric illnesses (n = 35). Mothers of age and gender-matched children with congenital hypothyroidism served as controls (n = 35). Psychological assessments were performed using structured questionnaires: the Parent Stress Scale, PRIME MD PHQ-9 Scale, and Ways of Coping Questionnaire. RESULTS: Mothers of children with DSD exhibited significantly higher mean (SD) stress levels [24.3 (4.25) vs 19.57 (1.89); P < 0.01]. Prevalence of depression prevalence was higher in mothers in the DSD group than in the hypothyroidism group (71% vs 42.9%, P < 0.001). Mothers of children with DSD also had poorer quality of life, and both high negative coping behavior and low positive coping behavior (P < 0.01) compared to controls, and stigma related to social exclusion was more pronounced. CONCLUSION: Beyond medical interventions, addressing family members' psychological well-being is essential in effectively managing DSD in the Indian context.

Clinical and molecular characterisation of children with monogenic obesity: a case series.

INTRODUCTION: To study the clinical profile and molecular diagnosis of children with severe early-onset non-syndromic monogenic obesity. METHODS: The clinical and molecular data (performed using whole exome sequencing) of 7 children with early-onset (< 5 years) non-syndromic monogenic obesity were extracted from the Obesity Clinic files and analysed retrospectively. RESULTS: The median (IQR) age at presentation was 18 (10.5-27) months. Of the 7 patients, 5 were boys, 3 had a history of parental consanguinity, and 4 had a family history of severe early-onset obesity. All patients exhibited hyperphagia and showed signs of insulin resistance. Dyslipidaemia and fatty liver were observed in 4. The variants identified in 6 patients included 2 in leptin receptor, and one each in melanocortin 4 receptor, pro-opiomelanocortin, leptin, and neurotrophic tyrosine kinase receptor type 2 genes. Notably, 4 of these variants were novel. CONCLUSIONS: This case series provides valuable insights into the spectrum of genetic mutations associated with non-syndromic monogenic obesity in North Indian children. The findings underscore the significance of next-generation sequencing in identifying the aetiology of severe early-onset obesity.

Underlying Roles of Polyol Additives in Promoting CO2 Capture in PEI/Silica Adsorbents.

Solid-supported amines having low molecular weight branched poly(ethylenimine) (PEI) physically impregnated into porous solid supports are promising adsorbents for CO2 capture. Co-impregnating short-chain poly(ethylene glycol) (PEG) together with PEI alters the performance of the adsorbent, delivering improved amine efficiency (AE, mol CO2 sorbed/mol N) and faster CO2 uptake rates. To uncover the physical basis for this improved gas capture performance, we probe the distribution and mobility of the polymers in the pores via small angle neutron scattering (SANS), solid-state NMR, and molecular dynamic (MD) simulation studies. SANS and MD simulations reveal that PEG displaces wall-bound PEI, making amines more accessible for CO2 sorption. Solid-state NMR and MD simulation suggest intercalation of PEG into PEI domains, separating PEI domains and reducing amine-amine interactions, providing potential PEG-rich and amine-poor interfacial domains that bind CO2 weakly via physisorption while providing facile pathways for CO2 diffusion. Contrary to a prior literature hypothesis, no evidence is obtained for PEG facilitating PEI mobility in solid supports. Instead, the data suggest that PEG chains coordinate to PEI, forming larger bodies with reduced mobility compared to PEI alone. We also demonstrate promising CO2 uptake and desorption kinetics at varied temperatures, facilitated by favorable amine distribution.

Enhanced Superconducting Diode Effect due to Coexisting Phases.

The superconducting diode effect refers to an asymmetry in the critical supercurrent J_{c}(n[over ^]) along opposite directions, J_{c}(n[over ^])≠J_{c}(-n[over ^]). While the basic symmetry requirements for this effect are known, it is, for junction-free systems, difficult to capture within current theoretical models the large current asymmetries J_{c}(n[over ^])/J_{c}(-n[over ^]) recently observed in experiment. We here propose and develop a theory for an enhancement mechanism of the diode effect arising from spontaneous symmetry breaking. We show-both within a phenomenological and a microscopic theory-that there is a coupling of the supercurrent and the underlying symmetry-breaking order parameter. This coupling can enhance the current asymmetry significantly. Our work might not only provide a possible explanation for recent experiments on trilayer graphene but also pave the way for future realizations of the superconducting diode effect with large current asymmetries.

Two-Legged Molecular Walker and Curvature: Mechanochemical Ring Migration on Graphene.

Attaining controllable molecular motion at the nanoscale can be beneficial for multiple reasons, spanning from optoelectronics to catalysis. Here we study the movement of a two-legged molecular walker by modeling the migration of a phenyl aziridine ring on curved graphene. We find that directional ring migration can be attained on graphene in the cases of both 1D (wrinkled/rippled) and 2D (bubble-shaped) curvature. Using a descriptor approach based on graphene's frontier orbital orientation, we can understand the changes in binding energy of the ring as it translates across different sites with variable curvature and the kinetic barriers associated with ring migration. Additionally, we show that the extent of covalent bonding between graphene and the molecule at different sites directly controls the binding energy gradient, propelling molecular migration. Importantly, one can envision such walkers as carriers of charge and disruptors of local bonding. This study enables a new way to tune the electronic structure of two-dimensional materials for a range of applications.

Prediction of transport behavior of nanoparticles using machine learning algorithm: Physical significance of important features.

There is a rising concern related to the possible risk of human exposure to nanoparticles (NPs). Several studies have reported on the transport behavior of NPs in the porous media under varying conditions. Thus, there is a scope to use this information in a predictive model so that the transport behavior of any un-explored NPs could be predicted. The main focus of his study, therefore, is to apply different machine learning (ML) based models to predict the transport efficiency of a wide range of NPs and to identify the important features. To achieve the objective, first, the dataset is prepared by extracting data from published papers for selected NPs [i.e., silver (nAg), titanium dioxide (nTiO2), zinc oxide (nZnO), graphene oxide (nGO), and etc.]. Then, random forest, XGBoost, and CatBoost algorithms combined with synthetic minority oversampling technique (SMOTE) were applied where retention fraction (RF) is considered as the target feature and particle characteristics (i.e., surface charge, size, concentration), solution chemistry [pH, ionic strength (IS]), porous media properties (grain size, porosity) and flow rate are considered as the training features. The outcome of the study indicates that CatBoost combined with SMOTE performed the best in predicting RF for the entire range of NPs (R2 > 0.89 and MSE < 0.007) as well as for individual NPs. Feature importance analysis indicates four features, namely zeta potential, IS, pH, and particle diameter (the entire range of NPs, nGO, nZnO) or grain size (nAg, nTiO2) have significant weightage (>75%). The result suggests that the features overrule the prediction of transport behavior rather than the types of individual NPs. The relative importance of the features depends on the range of the parameter used. The identified important features are in accordance with the underlying physical process, which makes the prediction model more reliable.

Precocious Puberty.

Precocious puberty is a common presentation to pediatricians with a significant overlap between physiology and pathology. While most girls with precocious puberty have no identifiable cause, boys are more likely to have a pathological cause. The trend of earlier onset of thelarche with slow pubertal tempo has led to a significant increase in the number of girls presenting with precocious puberty. Advanced growth, bone age, uterine maturation, and elevated LH suggest rapidly progressive puberty. The critical issues in evaluating a child presenting with precocious puberty include its confirmation, exclusion of physiological variants, identification of the cause, and determining the need for treatment. Step-wise evaluation with emphasis on clinical parameters provides cost-effective assessment. Gonadotropin-releasing hormone (GnRH) analogs remain the mainstay of treatment for central precocious puberty but should be restricted to individuals with rapidly progressive puberty and compromised final height. The management of rarer forms of peripheral precocious puberty (McCune Albright syndrome, congenital adrenal hyperplasia, and testotoxicosis) involves using experimental drugs under the guidance of specialists.

High-Throughput Computational Screening of Bioinspired Dual-Atom Alloys for CO2 Activation.

CO2 activation is an integral component of thermocatalytic and electrocatalytic CO2 conversion to liquid fuels and value-added chemicals. However, the thermodynamic stability of CO2 and the high kinetic barriers to activating CO2 are significant bottlenecks. In this work, we propose that dual atom alloys (DAAs), homo- and heterodimer islands in a Cu matrix, can offer stronger covalent CO2 binding than pristine Cu. The active site is designed to mimic the Ni-Fe anaerobic carbon monoxide dehydrogenase CO2 activation environment in a heterogeneous catalyst. We find that combinations of early transition metals (TMs) and late TMs embedded in Cu are thermodynamically stable and can offer stronger covalent CO2 binding than Cu. Additionally, we identify DAAs that have CO binding energies similar to Cu, both to avoid surface poisoning and to ensure attainable CO diffusion to Cu sites so that the C-C bond formation ability of Cu can be retained in conjunction with facile CO2 activation at the DAA sites. Machine learning feature selection reveals that the more electropositive dopants are primarily responsible for attaining the strong CO2 binding. We propose seven Cu-based DAAs and two single atom alloys (SAAs) with early TM late TM combinations, (Sc, Ag), (Y, Ag), (Y, Fe), (Y, Ru), (Y, Cd), (Y, Au), (V, Ag), (Sc), and (Y), for facile CO2 activation.

Creating hierarchical pores in metal-organic frameworks via postsynthetic reactions.

Metal-organic frameworks (MOFs) demonstrate promise for a multitude of applications owing to their high porosity and surface area. However, the majority of conventional MOFs possess only micropores with very limited accessibility to substances larger than 2 nm-especially functional biomacromolecules like some proteins. It is challenging to create an appropriately large pore size while avoiding framework collapse in MOFs. Herein, we present the generation of mesopores in microporous MOFs through three facile and effective techniques, namely Soxhlet washing, linker hydrolysis and linker thermolysis. These postsynthetic elimination approaches have been applied in selected MOFs, including PCN-250, PCN-160 and UiO-66, and controllably generate MOFs with hierarchical pores and high stability. Our work demonstrates reproducible and straightforward methods resulting in hierarchically porous materials that possess the benefits of mesoporosity while borrowing the robustness of a micropore framework. All the procedures can be conducted reliably at a multigram scale and operation time less than 6 h, representing a significant effort in the field of MOF synthesis. These hierarchically porous MOFs show great promise in a wide range of applications as efficient adsorbents, catalysts and drug carriers.

Photomechanochemical control over stereoselectivity in the [2 + 2] photodimerization of acenaphthylene.

Tuning solubility and mechanical activation alters the stereoselectivity of the [2 + 2] photochemical cycloaddition of acenaphthylene. Photomechanochemical conditions produce the syn cyclobutane, whereas the solid-state reaction in the absence of mechanical activation provides the anti. When the photochemical dimerization occurs in a solubilizing organic solvent, there is no selectivity. Dimerization in H2O, in which acenaphthylene is insoluble, provides the anti product. DFT calculations reveal that insoluble and solid-state reactions proceed via a covalently bonded excimer, which drives anti selectivity. Alternatively, the noncovalently bound syn conformer is more mechanosusceptible than the anti, meaning it experiences greater destabilization, thereby producing the syn product under photomechanochemical conditions. Cyclobutanes are important components of biologically active natural products and organic materials, and we demonstrate stereoselective methods for obtaining syn or anti cyclobutanes under mild conditions and without organic solvents. With this work, we validate photomechanochemistry as a viable new direction for the preparation of complex organic scaffolds.

Clinicopathological alteration of symptoms with serotype among dengue infected pediatric patients.

Dengue fever is a self-limiting, acute febrile illness caused by an arbovirus. This infection may be asymptomatic or symptomatic with its potential life-threatening form as DHF/DSS. Severe dengue cases occur typically in children due to overproduction of proinflammatory and anti-inflammatory cytokines (called cytokines storm) as well as increased microvascular permeability in them. This study aimed to find circulating dengue serotype and their clinicopathological association among pediatric patients admitted to tertiary care hospitals in Kolkata, India. Overall, 210 patients were approached, among them, 170 dengue suspected children admitted to three tertiary care hospitals were included in this study. Dengue samples were screened for the presence of dengue NS1 antigen and IgM antibodies by enzyme-linked immunosorbent assay. Viral RNA was extracted from NS1 seropositive serum samples and subjected to molecular serotyping by semi-nested reverse-transcription polymerase chain reaction. All patients were followed up for clinical manifestations and biochemical parameters associated with dengue. Cocirculation of all four serotypes was observed and DENV2 was the major circulating strain. Physiological classification of associated clinical symptoms was done as per WHO guideline and represented as a percentage variable. A multivariate logistic regression approach was used for making a regression model including dengue-associated clinical symptoms with dengue positivity or negativity as dependent variables. Thrombocytopenia was observed in 69% of patients and the commonest bleeding manifestation was petechia. Liver function profiles of infected patients were observed during follow-up and represented using a box plot. A significant change in trends of dengue-associated clinical manifestations and differential expression of liver functional profile with different phases of transition of dengue fever was observed in this study population.

Mechanochemical Molecular Migration on Graphene.

In this study, we propose that the curvature of graphene can be exploited to perform directional molecular motion and provide atomistic insights into the curvature-dependent molecular migration through density functional theory calculations. We first reveal the origin of the different migration trends observed experimentally for aromatic molecules with electron-donating and -withdrawing groups on p-doped functionalized graphene. Next, we show that the kinetic barrier for migration depends on the amount and nature of the curvature, that is, positive versus negative curvature. We find that the molecular migration on a wrinkled/rippled graphene sheet preferentially happens from the valley (positive curvature) to the mountain (negative curvature) regions. To understand the origin of such curvature-dependent molecular motion and migrational kinetic barrier trends, we develop a descriptor based on the frontier orbital orientation of graphene. Finally, based on these findings, we predict that time- and space-varying curvature can drive directional molecular motion on graphene and thus further propose that efforts should focus on exploring other two-dimensional materials as active platforms for performing controlled molecular motion.

Unexpected structural/motional mode of water intercalated into an α-crystalline zirconium phosphate deduced by 31 P and 2 H solid-state MAS NMR spectra.

In developing the approach to understanding dynamics of intercalates in layered materials, crystalline-layered zirconium phosphate Zr (HPO4 )2 ·0.35D2 O has been prepared and characterized by the 1 H, 31 P, and 2 H solid-state MAS NMR spectra, including 31 P and 2 H T1 measurements. At temperatures >253 K, the intercalated water shows two spectrally-distinguished deuterons unprecedentedly with different DQCC's and 2 H T1 times, one of which is hydrogen bonded. The collected data allowed to identify an unexpected bonding/dynamic mode of water molecules, which experience fast rotation around the hydrogen bond, formed with a zirconium-coordinated oxygen. The low-temperature 2 H MAS NMR experiments have demonstrated the presence of additional hydrogen bond P(H)O˙˙˙ DO, population of which grows on cooling to 195 K corresponding to the doubly hydrogen-bonded immobile water molecule.

Unraveling the Elastic Properties of (Quasi)Two-Dimensional Hybrid Perovskites: A Joint Experimental and Theoretical Study.

The unique properties of hybrid organic-inorganic perovskites (HOIPs) promise to open doors to next-generation flexible optoelectronic devices. Before such advances are realized, a fundamental understanding of the mechanical properties of HOIPs is required. Here, we combine ab initio density functional theory (DFT) modeling with a diverse set of experiments to study the elastic properties of (quasi)2D HOIPs. Specifically, we focus on (quasi)2D single crystals of phenethylammonium methylammonium lead iodide, (PEA)2PbI4(MAPbI3)n-1, and their 3D counterpart, MAPbI3. We used nanoindentation (both Hertzian and Oliver-Pharr analyses) in combination with elastic buckling instability experiments to establish the out-of-plane and in-plane elastic moduli. The effect of Van der Waals (vdW) forces, different interlayer interactions, and finite temperature are combined with DFT calculations to accurately model the system. Our results reveal a nonmonotonic dependence of both the in-plane and out-of plane elastic moduli on the number of inorganic layers (n) rationalized by first-principles calculations. We discuss how the presence of defects in as-grown crystals and macroscopic interlayer deformations affect the mechanical response of (quasi)2D HOIPs. Comparing the in- and out-of-plane experimental results with the theory reveals that perturbations to the covalent and ionic bonds (which hold a 2D layer together) is responsible for the relative out-of-plane stiffness of these materials. In contrast, we conjecture that the in-plane softness originates from macroscopic or mesoscopic motions between 2D layers during buckling experiments. Additionally, we learn how dispersion and π interactions in organic bilayers can have a determining role in the elastic response of the materials, especially in the out-of-plane direction. The understanding gained by comparing ab initio and experimental techniques paves the way for rational design of layered HOIPs with mechanical properties favorable for strain-intensive applications. Combined with filters for other favorable criteria, e.g., thermal or moisture stability, one can systematically screen viable (quasi)2D HOIPs for a variety of flexible optoelectronic applications.

A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation.

Design of asymmetric catalysts generally involves time- and resource-intensive heuristic endeavors. In view of the steady increase in interest toward efficient catalytic asymmetric reactions and the rapid growth in the field of machine learning (ML) in recent years, we envisaged dovetailing these two important domains. We selected a set of quantum chemically derived molecular descriptors from five different asymmetric binaphthyl-derived catalyst families with the propensity to impact the enantioselectivity of asymmetric hydrogenation of alkenes and imines. The predictive power of the random forest (RF) built using the molecular parameters of a set of 368 substrate-catalyst combinations is found to be impressive, with a root-mean-square error (rmse) in the predicted enantiomeric excess (%ee) of about 8.4 ± 1.8 compared to the experimentally known values. The accuracy of RF is found to be superior to other ML methods such as convolutional neural network, decision tree, and eXtreme gradient boosting as well as stepwise linear regression. The proposed method is expected to provide a leap forward in the design of catalysts for asymmetric transformations.