Lifting Hofmeister's Curse: Impact of Cations on Diffusion, Hydrogen Bonding, and Clustering of Water.

Water plays a role in the stability, reactivity, and dynamics of the solutes that it contains. The presence of ions alters this capacity by changing the dynamics and structure of water. However, our understanding of how and to what extent this occurs is still incomplete. Here, a study of the low-frequency Raman spectra of aqueous solutions of various cations by using optical Kerr-effect spectroscopy is presented. This technique allows for the measurement of the changes that ions cause in both the diffusive dynamics and the vibrations of the hydrogen-bond structure of water. It is found that when salts are added, some of the water molecules become part of the ion solvation layers, while the rest retain the same diffusional properties as those of pure water. The slowing of the dynamics of the water molecules in the solvation shell of each ion was found to depend on its charge density at infinite dilution conditions and on its position in the Hofmeister series at higher concentrations. It is also observed that all cations weaken the hydrogen-bond structure of the solution and that this weakening depends only on the size of the cation. Finally, evidence is found that ions tend to form amorphous aggregates, even at very dilute concentrations. This work provides a novel approach to water dynamics that can be used to better study the mechanisms of solute nucleation and crystallization, the structural stability of biomolecules, and the dynamic properties of complex solutions, such as water-in-salt electrolytes.

A Second Glass Transition Observed in Single-Component Homogeneous Liquids Due to Intramolecular Vitrification.

On supercooling a liquid, the viscosity rises rapidly until at the glass transition it vitrifies into an amorphous solid accompanied by a steep drop in the heat capacity. Therefore, a pure homogeneous liquid is not expected to display more than one glass transition. Here we show that a family of single-component homogeneous molecular liquids, titanium tetraalkoxides, exhibit two calorimetric glass transitions of comparable magnitude, one of which is the conventional glass transition associated with dynamic arrest of the bulk liquid properties, while the other is associated with the freezing out of intramolecular degrees of freedom. Such intramolecular vitrification is likely to be found in molecules in which low-frequency terahertz intramolecular motion is coupled to the surrounding liquid. These results imply that intramolecular barrier-crossing processes, typically associated with chemical reactivity, do not necessarily follow the Arrhenius law but may freeze out at a finite temperature.

Key considerations, target product profiles, and research gaps in the application of infrared spectroscopy and artificial intelligence for malaria surveillance and diagnosis.

Studies on the applications of infrared (IR) spectroscopy and machine learning (ML) in public health have increased greatly in recent years. These technologies show enormous potential for measuring key parameters of malaria, a disease that still causes about 250 million cases and 620,000 deaths, annually. Multiple studies have demonstrated that the combination of IR spectroscopy and machine learning (ML) can yield accurate predictions of epidemiologically relevant parameters of malaria in both laboratory and field surveys. Proven applications now include determining the age, species, and blood-feeding histories of mosquito vectors as well as detecting malaria parasite infections in both humans and mosquitoes. As the World Health Organization encourages malaria-endemic countries to improve their surveillance-response strategies, it is crucial to consider whether IR and ML techniques are likely to meet the relevant feasibility and cost-effectiveness requirements-and how best they can be deployed. This paper reviews current applications of IR spectroscopy and ML approaches for investigating malaria indicators in both field surveys and laboratory settings, and identifies key research gaps relevant to these applications. Additionally, the article suggests initial target product profiles (TPPs) that should be considered when developing or testing these technologies for use in low-income settings.

Evaluation of diffuse reflectance spectroscopy for predicting age, species, and cuticular resistance of Anopheles gambiae s.l under laboratory conditions.

Mid-infrared spectroscopy (MIRS) combined with machine learning analysis has shown potential for quick and efficient identification of mosquito species and age groups. However, current technology to collect spectra is destructive to the sample and does not allow targeting specific tissues of the mosquito, limiting the identification of other important biological traits such as insecticide resistance. Here, we assessed the use of a non-destructive approach of MIRS for vector surveillance, micro diffuse reflectance spectroscopy (µDRIFT) using mosquito legs to identify species, age and cuticular insecticide resistance within the Anopheles gambiae s.l. complex. These mosquitoes are the major vectors of malaria in Africa and the focus on surveillance in malaria control programs. Legs required significantly less scanning time and showed more spectral consistence compared to other mosquito tissues. Machine learning models were able to identify An. gambiae and An. coluzzii with an accuracy of 0.73, two ages groups (3 and 10 days old) with 0.77 accuracy and we obtained accuracy of 0.75 when identifying cuticular insecticide resistance. Our results highlight the potential of different mosquito tissues and µDRIFT as tools for biological trait identification on mosquitoes that transmit malaria. These results can guide new ways of identifying mosquito traits which can help the creation of innovative surveillance programs by adapting new technology into mosquito surveillance and control tools.

Kinetic study on the reaction of sodium nitrite with neurotransmitters secreted in the stomach.

Nitroso-compounds are potentially mutagenic and carcinogenic compounds due to their ability to alkylate DNA bases. One of the most common sources of human exposure to nitroso-compounds is their formation in the acidic environment of the stomach by the reaction between electron-rich molecules present in the lumen and sodium nitrite ingested in the diet. To date, the formation of nitroso-compounds by the reaction of nitrite with food components has been investigated in depth, but little attention has been paid to substances secreted in the stomach, such as dopamine or serotonin, whose reaction products with nitrite have proven mutagenic properties. In this article, we present a kinetic study with UV-visible spectroscopy of the nitrosation reactions of both molecules, as well as of L-tyrosine, the amino-acid precursor of dopamine. We determined the kinetic parameters and reaction mechanisms for the reactions, studying the influence of the reactants concentration, pH, temperature, and ionic strength on the reaction rate. In all cases, the favoured reaction product was a stable nitroso-compound. Serotonin, the molecule whose product was the most mutagenic, underwent two consecutive nitrosation reactions. These findings suggest that additional biological research is needed to understand how this reaction alters the function of these neurotransmitters as well as the potentially toxic effects they may have once nitrosated.

Understanding the emergence of the boson peak in molecular glasses.

A common feature of glasses is the "boson peak", observed as an excess in the heat capacity over the crystal or as an additional peak in the terahertz vibrational spectrum. The microscopic origins of this peak are not well understood; the emergence of locally ordered structures has been put forward as a possible candidate. Here, we show that depolarised Raman scattering in liquids consisting of highly symmetric molecules can be used to isolate the boson peak, allowing its detailed observation from the liquid into the glass. The boson peak in the vibrational spectrum matches the excess heat capacity. As the boson peak intensifies on cooling, wide-angle x-ray scattering shows the simultaneous appearance of a pre-peak due to molecular clusters consisting of circa 20 molecules. Atomistic molecular dynamics simulations indicate that these are caused by over-coordinated molecules. These findings represent an essential step toward our understanding of the physics of vitrification.

Using transfer learning and dimensionality reduction techniques to improve generalisability of machine-learning predictions of mosquito ages from mid-infrared spectra.

BACKGROUND: Old mosquitoes are more likely to transmit malaria than young ones. Therefore, accurate prediction of mosquito population age can drastically improve the evaluation of mosquito-targeted interventions. However, standard methods for age-grading mosquitoes are laborious and costly. We have shown that Mid-infrared spectroscopy (MIRS) can be used to detect age-specific patterns in mosquito cuticles and thus can be used to train age-grading machine learning models. However, these models tend to transfer poorly across populations. Here, we investigate whether applying dimensionality reduction and transfer learning to MIRS data can improve the transferability of MIRS-based predictions for mosquito ages. METHODS: We reared adults of the malaria vector Anopheles arabiensis in two insectaries. The heads and thoraces of female mosquitoes were scanned using an attenuated total reflection-Fourier transform infrared spectrometer, which were grouped into two different age classes. The dimensionality of the spectra data was reduced using unsupervised principal component analysis or t-distributed stochastic neighbour embedding, and then used to train deep learning and standard machine learning classifiers. Transfer learning was also evaluated to improve transferability of the models when predicting mosquito age classes from new populations. RESULTS: Model accuracies for predicting the age of mosquitoes from the same population as the training samples reached 99% for deep learning and 92% for standard machine learning. However, these models did not generalise to a different population, achieving only 46% and 48% accuracy for deep learning and standard machine learning, respectively. Dimensionality reduction did not improve model generalizability but reduced computational time. Transfer learning by updating pre-trained models with 2% of mosquitoes from the alternate population improved performance to ~ 98% accuracy for predicting mosquito age classes in the alternative population. CONCLUSION: Combining dimensionality reduction and transfer learning can reduce computational costs and improve the transferability of both deep learning and standard machine learning models for predicting the age of mosquitoes. Future studies should investigate the optimal quantities and diversity of training data necessary for transfer learning and the implications for broader generalisability to unseen datasets.

Asymmetric Reflection Induced in Reciprocal Hyperbolic Materials.

Reflection is one of the most fundamental properties of light propagation. The ability to engineer this property can be a powerful tool when constructing a variety of now ubiquitous optical and electronic devices, including one-way mirrors and antennas. Here, we show from both experimental and theoretical evidence that highly asymmetric reflection can be induced in reciprocal hyperbolic materials. This asymmetry stems from the asymmetric cross-polarization conversion between two linearly polarized waves, an intrinsic and more exotic property of hyperbolic media that is bereft of research. In addition to angle-controllable reflection, our findings suggest that optical devices could utilize the polarization of the incident beam, or even the polarization of the output wave, to engineer functionality; additionally, in hyperbolic slabs or films, the asymmetry can be tailored by controlling the thickness of the material. Such phenomena are key for directional-dependent optical and optoelectronic devices.

Rapid age-grading and species identification of natural mosquitoes for malaria surveillance.

The malaria parasite, which is transmitted by several Anopheles mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of mosquito vectors. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We present a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquito cuticle that simultaneously identifies the species and age class of three main malaria vectors in natural populations. Using spectra from over 40, 000 ecologically and genetically diverse An. gambiae, An. arabiensis, and An. coluzzii females, we develop a deep transfer learning model that learns and predicts the age of new wild populations in Tanzania and Burkina Faso with minimal sampling effort. Additionally, the model is able to detect the impact of simulated control interventions on mosquito populations, measured as a shift in their age structures. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.

Low-frequency vibrational modes in G-quadruplexes reveal the mechanical properties of nucleic acids.

Low-frequency vibrations play an essential role in biomolecular processes involving DNA such as gene expression, charge transfer, drug intercalation, and DNA-protein recognition. However, understanding the vibrational basis of these mechanisms relies on theoretical models due to the lack of experimental evidence. Here we present the low-frequency vibrational spectra of G-quadruplexes (structures formed by four strands of DNA) and B-DNA characterized using femtosecond optical Kerr-effect spectroscopy. Contrary to expectation, we found that G-quadruplexes show several strongly underdamped delocalized phonon-like modes that have the potential to contribute to the biology of the DNA at the atomic level. In addition, G-quadruplexes present modes at a higher frequency than B-DNA demonstrating that changes in the stiffness of the molecule alter its gigahertz to terahertz vibrational profile.

Low-Frequency (Gigahertz to Terahertz) Depolarized Raman Scattering Off n-Alkanes, Cycloalkanes, and Six-Membered Rings: A Physical Interpretation.

Molecular liquids have long been known to undergo various distinct intermolecular motions, from fast librations and cage-rattling oscillations to slow orientational and translational diffusion. However, their resultant gigahertz to terahertz spectra are far from simple, appearing as broad shapeless bands that span many orders of magnitude of frequency, making meaningful interpretation troublesome. Ad hoc spectral line shape fitting has become a notoriously fine art in the field; a unified approach to handling such spectra is long overdue. Here we apply ultrafast optical Kerr-effect (OKE) spectroscopy to study the intermolecular dynamics of room-temperature n-alkanes, cycloalkanes, and six-carbon rings, as well as liquid methane and propane. This work provides stress tests and converges upon an experimentally robust model across simple molecular series and range of temperatures, providing a blueprint for the interpretation of the dynamics of van der Waals liquids. This will enable the interpretation of low-frequency spectra of more complex liquids.

Prediction of mosquito species and population age structure using mid-infrared spectroscopy and supervised machine learning.

Despite the global efforts made in the fight against malaria, the disease is resurging. One of the main causes is the resistance that Anopheles mosquitoes, vectors of the disease, have developed to insecticides. Anopheles must survive for at least 10 days to possibly transmit malaria. Therefore, to evaluate and improve malaria vector control interventions, it is imperative to monitor and accurately estimate the age distribution of mosquito populations as well as their population sizes. Here, we demonstrate a machine-learning based approach that uses mid-infrared spectra of mosquitoes to characterise simultaneously both age and species identity of females of the African malaria vector species Anopheles gambiae and An. arabiensis, using laboratory colonies. Mid-infrared spectroscopy-based prediction of mosquito age structures was statistically indistinguishable from true modelled distributions. The accuracy of classifying mosquitoes by species was 82.6%. The method has a negligible cost per mosquito, does not require highly trained personnel, is rapid, and so can be easily applied in both laboratory and field settings. Our results indicate this method is a promising alternative to current mosquito species and age-grading approaches, with further improvements to accuracy and expansion for use with wild mosquito vectors possible through collection of larger mid-infrared spectroscopy data sets.

Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis.

BACKGROUND: The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Human Blood Index (HBI), currently requires expensive and time-consuming laboratory procedures involving enzyme-linked immunosorbent assays (ELISA) or polymerase chain reactions (PCR). Here, mid-infrared (MIR) spectroscopy and supervised machine learning are used to accurately distinguish between vertebrate blood meals in guts of malaria mosquitoes, without any molecular techniques. METHODS: Laboratory-reared Anopheles arabiensis females were fed on humans, chickens, goats or bovines, then held for 6 to 8 h, after which they were killed and preserved in silica. The sample size was 2000 mosquitoes (500 per host species). Five individuals of each host species were enrolled to ensure genotype variability, and 100 mosquitoes fed on each. Dried mosquito abdomens were individually scanned using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra (4000 cm-1 to 400 cm-1). The spectral data were cleaned to compensate atmospheric water and CO2 interference bands using Bruker-OPUS software, then transferred to Python™ for supervised machine-learning to predict host species. Seven classification algorithms were trained using 90% of the spectra through several combinations of 75-25% data splits. The best performing model was used to predict identities of the remaining 10% validation spectra, which had not been used for model training or testing. RESULTS: The logistic regression (LR) model achieved the highest accuracy, correctly predicting true vertebrate blood meal sources with overall accuracy of 98.4%. The model correctly identified 96% goat blood meals, 97% of bovine blood meals, 100% of chicken blood meals and 100% of human blood meals. Three percent of bovine blood meals were misclassified as goat, and 2% of goat blood meals misclassified as human. CONCLUSION: Mid-infrared spectroscopy coupled with supervised machine learning can accurately identify multiple vertebrate blood meals in malaria vectors, thus potentially enabling rapid assessment of mosquito blood-feeding histories and vectorial capacities. The technique is cost-effective, fast, simple, and requires no reagents other than desiccants. However, scaling it up will require field validation of the findings and boosting relevant technical capacity in affected countries.

Frecuencia del síndrome metabólico y factores de riesgo en adultos con y sin diabetes mellitus e hipertensión arterial / Frequency of metabolic syndrome and risk factors in adults with and without diabetes mellitus and arterial hypertension

RESUMEN Objetivo Determinar la frecuencia y factores de riesgo para Síndrome Metabólico (SM) en adultos con Diabetes mellitus, hipertensión arterial y sin diabetes-hipertensión. Material y Métodos Se realizó un estudio transversal analítico en derechohabientes de ambos sexos y mayores de 20 años de los servicios de consulta externa del HGZ No. 1 IMSS Colima, México. Las variables estudiadas fueron edad, IMC, diámetro de la cintura, grado de escolaridad, estado socioeconómico, grado de actividad física, tabaquismo, antecedentes familiares para diabetes e hipertensión arterial (HTA) y parámetros bioquímicos como glucosa, colesterol HDL, triglicéridos. Resultados Se estudiaron 417 pacientes (170 hombres y 247 mujeres), con un promedio de edad 53,2 ± 13,4 años (intervalo 20 a 86 años). La frecuencia global del SM fue del 52,3 % (56 % mujeres y 46,4 % hombres). Mientras que la frecuencia del SM fue de 50 % en DM2, 42% en HTA, 80 % DM2 + HTA y 28,2 % sin DM o HTA. La frecuencia del tabaquismo fue del 27,8 % y fue un factor de riesgo importante para la totalidad de pacientes con SM, en DM2 y en DM2+HAT. Conclusiones La frecuencia del SM en adultos fue del 52,3 %, las mujeres fueron más afectadas y el tabaquismo fue el factor de riesgo más importante.(AU)

ABSTRACT Objective Determinate the frequency and the risk factors for Metabolic Syndrome in adults with diabetes mellitus, Hypertension and without Diabetes- Hypertension. Materials and Methods We realized a cross-sectional study in patients of both sexes and older than 20 years of the "Hospital General de Zona 1 IMSS" in Colima, Mexico. The variables studied were: age, BMI, waist circumference, cigarette smoking, and family history of diabetes and hypertension, and biochemical parameters, such as glucose, HDL cholesterol and triglycerides. Results A total of 417 persons were enrolled (170 men and 247 women), with an age average of 53.2 ± 13.4 years (age range, 20 to 86 years). The global frequency of the metabolic syndrome was 52.3 % (56 % in women and 46.4 % in men). While the MS frequency was 50 % in DM2, 42 % in hypertension, 80 % in DM2+hypertension and 28.2 % without DM2 and hypertension. The cigarette smoking frequency was 27.8 %, and it was an important risk factor for the totally of patients with MS, in DM2 and in DM2+hypertension. Conclusions The frequency of MS in adults was 52.3 %, women were the most affected, and cigarette smoking was the most important risk factor.(AU)

Spectrum of Slow and Super-Slow (Picosecond to Nanosecond) Water Dynamics around Organic and Biological Solutes.

Water dynamics in the solvation shell of solutes plays a very important role in the interaction of biomolecules and in chemical reaction dynamics. However, a selective spectroscopic study of the solvation shell is difficult because of the interference of the solute dynamics. Here we report on the observation of heavily slowed down water dynamics in the solvation shell of different solutes by measuring the low-frequency spectrum of solvation water, free from the contribution of the solute. A slowdown factor of ∼50 is observed even for relatively low concentrations of the solute. We go on to show that the effect can be generalized to different solutes including proteins.

Phonon-like Hydrogen-Bond Modes in Protic Ionic Liquids.

Gigahertz- to terahertz-frequency infrared and Raman spectra contain a wealth of information concerning the structure, intermolecular forces, and dynamics of ionic liquids. However, these spectra generally have a large number of contributions ranging from slow diffusional modes to underdamped librations and intramolecular vibrational modes. This makes it difficult to isolate effects such as the role of Coulombic and hydrogen-bonding interactions. We have applied far-infrared and ultrafast optical Kerr effect spectroscopies on carefully selected ions with a greater or lesser degree of symmetry in order to isolate spectral signals of interest. This has allowed us to demonstrate the presence of longitudinal and transverse optical phonon modes and a great similarity of alkylammonium-based protic ionic liquids to liquid water. The data show that such phonon modes will be present in all ionic liquids, requiring a reinterpretation of their spectra.

Ultrafast 2D-IR and optical Kerr effect spectroscopy reveal the impact of duplex melting on the structural dynamics of DNA.

Changes in the structural and solvation dynamics of a 15mer AT DNA duplex upon melting of the double-helix are observed by a combination of ultrafast two-dimensional infrared (2D-IR) and optical Kerr-effect (OKE) spectroscopies. 2D-IR spectroscopy of the vibrational modes of the DNA bases reveal signature off-diagonal peaks arising from coupling and energy transfer across Watson-Crick paired bases that are unique to double-stranded DNA (ds-DNA). Spectral diffusion of specific base vibrational modes report on the structural dynamics of the duplex and the minor groove, which is predicted to contain a spine of hydration. Changes in these dynamics upon melting are assigned to increases in the degree of mobile solvent access to the bases in single-stranded DNA (ss-DNA) relative to the duplex. OKE spectra exhibit peaks that are assigned to specific long-range phonon modes of ds- and ss-DNA. Temperature-related changes in these features correlate well with those obtained from the 2D-IR spectra although the melting temperature of the ds-DNA phonon band is slightly higher than that for the Watson-Crick modes, suggesting that a degree of long-range duplex structure survives the loss of Watson-Crick hydrogen bonding. These results demonstrate that the melting of ds-DNA disrupts helix-specific structural dynamics encompassing length scales ranging from mode delocalisation in the Watson-Crick base pairs to long-range phonon modes that extend over multiple base pairs and which may play a role in molecular recognition of DNA.

Frustration of crystallisation by a liquid-crystal phase.

Frustration of crystallisation by locally favoured structures is critically important in linking the phenomena of supercooling, glass formation, and liquid-liquid transitions. Here we show that the putative liquid-liquid transition in n-butanol is in fact caused by geometric frustration associated with an isotropic to rippled lamellar liquid-crystal transition. Liquid-crystal phases are generally regarded as being "in between" the liquid and the crystalline state. In contrast, the liquid-crystal phase in supercooled n-butanol is found to inhibit transformation to the crystal. The observed frustrated phase is a template for similar ordering in other liquids and likely to play an important role in supercooling and liquid-liquid transitions in many other molecular liquids.

Mutagenic products are promoted in the nitrosation of tyramine.

Tyramine is a biogenic compound derived from the decarboxylation of the amino acid tyrosine, and is therefore present at important concentrations in a broad range of raw and fermented foods. Owing to its chemical properties, tyramine can react with nitrite, a common food additive, in the acidic medium of stomach to form N- and C-nitroso compounds. Since toxicology studies have shown that the product of C-nitrosation of tyramine is mutagenic, in the present article tyramine nitrosation mechanisms have been characterized in order to discern which of them are favoured under conditions similar to those in the human stomach lumen. To determine the kinetic course of nitrosation reactions, a systematic study of the nitrosation of ethylbenzene, phenethylamine, and tyramine was carried out, using UV-visible absorption spectroscopy. The results show that, under conditions mimicking those of the stomach lumen, the most favoured reaction in tyramine is C-nitrosation, which generates mutagenic products.