Browse Articles

Development of a dynamical model to enhance understanding of epidemiology of schistosomiasis in school-aged-children

Abstract School-aged-children are most vulnerable to schistosomiasis as exemplified by age-prevalence profiles although current understanding of these patterns needs improvement. Using epidemiological data from the southern shoreline of Lake Malawi, we investigated the dynamics of Schistosoma transmission and the main determinants of Schistosoma infection risk using a transmission dynamic model, considering urogenital and intestinal schistosomiasis respectively. Specifically, we assessed whether the proximity of primary schools to the immediate shoreline was a major geospatial and epidemiological determinant. Cross-sectional parasitology and malacological data previously collected and analysed was used, including age-infection profiles and interpolated predicted snail distributions for the southern part of Lake Malawi shoreline. A disease SEIRS ordinary differential equation model was created, and an observation prevalence model was formed using a binomial sampling distribution using the already published dataset. An optimisation using L-BFGS-B algorithm with upper/lower bounded box constraints was carried out to calibrate the model to find the best parameter values for each infection state transition given the disease model and dataset. The aim was to recapture the age-structure dynamics shown in the observation model representing the already published age-infection profiles. Concerning intestinal schistosomiasis, the best model for Biomphalaria sp. was the use of a single transmission rate for all the school’s and no spatial effect. By contrast, for urogenital schistosomiasis, the best model for Bulinus spp. was found when using an independent transmission rate for each school and no spatial effect. There was some evidence that we were able to capture the age-structured dynamics of infection in SAC despite the expected outcome differing to statistical output due to sparse data. Within our study area, we found there was no significant effect on SAC exposure to Schistosoma infection risk based on school distance from the shoreline. Further, there was heterogeneity between schools in transmission rates estimated, although these did not have significantly different confidence intervals. However, schools considered in our study were all relatively close to cercaria infested shorelines. Further studies using a longitudinal cohort study could improve understanding of Schistosoma infection dynamics and allow for improved control method application.

De Novo Design of High‐Affinity Miniprotein Binders Targeting <i>Francisella Tularensis</i> Virulence Factor

Abstract Francisella tularensis poses considerable public health risk due to its high infectivity and potential for bioterrorism. Francisella‐like lipoprotein (Flpp3), a key virulence factor unique to Francisella, plays critical roles in infection and immune evasion, making it a promising target for therapeutic development. However, the lack of well‐defined binding pockets and structural information on native interactions has hindered structure‐guided ligand discovery against Flpp3. Here, we used a combination of physics‐based and deep‐learning methods to design high‐affinity miniprotein binders targeting two distinct sites on Flpp3. We identified four binders for site I with binding affinities ranging between 24–110 nM. For the second site, an initial binder showed a dissociation constant ( K D ) of 81 nM, and subsequent site saturation mutagenesis yielded variants with sub‐nanomolar affinities. Circular dichroism confirmed the topology of designed miniproteins. The X‐ray crystal structure of Flpp3 in complex with a site I binder is nearly identical to the design model (Cα root‐mean‐square deviation (RMSD): 0.9 Å). These designed miniproteins provide research tools to explore the roles of Flpp3 in tularemia and should enable the development of new therapeutic candidates.

Bridging pleiotropic mechanisms in leprosy type-1 reactions and neurodegenerative diseases

Abstract Leprosy is an infectious disease of the skin and peripheral nervous system. Sudden episodes of hyperinflammation, known as Type 1 Reactions (T1R), are a main contributor to permanent nerve damage in leprosy. The genetic component associated with the neuro-inflammatory phenotype of T1R displays pleiotropic effects with Parkinson’s disease (PD). In this study, we explored the genetic overlap between PD and T1R and expanded the evaluation of pleiotropic effects between T1R and other neurodegenerative disorders. We replicated the association of PD-linked rare variants in PRKN with T1R in Vietnamese leprosy patients. Analysis of 24 PD associated-genes revealed compound effects between rare protein-altering variants and T1R in the interacting genes PRKN/PINK1 ( P  = 2.7 –05 ; OR = 4.0) and a combination of rare/low frequency variants in the LRRK2/GAK pair ( P  = 6.7 –05 ; OR = 0.54). These findings validated a genetic overlap between T1R and PD with two distinct axes, one of shared risk via PRKN/PINK1 and a second of antagonistic pleiotropic via LRRK2/GAK . When testing an additional 94 genes associated with neurodegenerative diseases we identified variants in the amyotrophic lateral sclerosis disease-linked gene TBK1 associated with T1R ( P  = 0.004; OR = 12.9). Our results highlight shared biological processes between leprosy and neurodegenerative diseases, which may indicate candidate drugs for repurposing to improve T1R management.

Evaluation of natural radioactivity and radiological hazards in concrete aggregates for radiation shielding applications

Thermally Activated Stereoinversion in Benzotrithiophene‐Based Supramolecular Polymers with Water as the Effector

Abstract We report the synthesis of the chiral benzotrithiophene (BTT) derivative 1 , which self‐assembles into helical supramolecular polymers. The C 3 symmetry of BTT 1 enables a unique temperature‐dependent stereomutation process in which water acts as a key effector. Circular dichroism measurements reveal that a higher water content induces a double stereomutation, ultimately restoring the original helicity. In contrast, reducing the water content leads to a single, stable stereomutation, which duration depends on the amount of water in solution. To elucidate the stereomutation mechanism, we conducted a multi‐level computational study, identifying two main conformations for the monomeric species that lead to diastereomeric helices with different preferred handedness, thus resulting in opposite dichroic patterns. The transition between these aggregates involves rotation of the BTT cores and amide groups, a process significantly facilitated by water molecules via triple hydrogen bonding within the helical stack. These findings exhibit the crucial role of the solvent in modulating chiral supramolecular polymer structures and provide unprecedented insights into the influence of water on self‐assembly and stereomutation processes.

Enhancement in production efficiency using DMAIC methodology of six sigma

Selective Reductive Amination of Carbonyls to Primary Amines Under Ambient Conditions Over Rh/MFM‐300(Cr)

Abstract The synthesis of organic amines via reductive amination of biomass‐derived carbonyl compounds is an important target for sustainable chemical industries. The control of selectivity for the formation of primary amines versus secondary amines is challenging, and high temperature and pressures using H 2 are required to generate the desired selectivity. Herein, we report the highly selective reductive amination of a broad range of aldehydes and ketones by NH 3 and H 2 over Rh/MFM‐300(Cr) to form primary amines with a selectivity of up to 99% under ambient conditions. Inelastic neutron scattering reveals that the Rh species not only promote the hydrogenation process, but also catalyzes the ammonolysis of the Schiff base intermediate, facilitating the selective synthesis of primary amines. This protocol achieves selective reductive amination at 25 °C and 1 atm, providing an energy‐efficient route to a broad spectrum of amines.

Correction: Determinants of care-seeking for ARI/Pneumonia-like symptoms among under-2 children in urban slums in and around Dhaka City, Bangladesh

Cationic‐Catalyst Strategy Enabling Ultrafast and Controlled Polymerization and Efficient Depolymerization Toward a Circular Polymer Economy

Abstract The development of chemically recyclable polymers is considered one of the ideal solutions to alleviate the growing polymer waste. Fast, controlled synthesis and closed‐loop recycling of amino acid (AA) polymers, as an important class of polyamides, are highly important for applications and circular polymer economy. Strong base‐initiated N ‐carboxyanhydrides (NCAs) polymerization can prepare AA polymers very quickly, however, has the long‐standing challenge of poor molecular weight controllability and numerous side reactions. Herein, we develop a highly efficient cationic‐catalyst strategy that enables both “ultrafast, controlled AA polymer synthesis” and “closed‐loop AA polymer recycling”. This robust and easily scalable cationic‐catalyst strategy is compatible with various organic strong bases as initiators, and enables the synthesis of molecular weight well‐controlled AA polymers on a hectogram scale (∼120 g) within minutes. Moreover, the cationic catalyst facilitates the efficient depolymerization of AA polymers into environmentally friendly amino acids, with a recovery rate of 85.9%. In addition, the catalyst itself can be recycled quantitatively. In short, the highly efficient cationic‐catalyst strategy implies practical and promising applications in polymerization and depolymerization chemistry, demonstrating great potential for the circular polymer economy and sustainable management of polymer waste.

Design and optimization of intranasal aripiprazole-loaded nanostructured lipid carriers for enhanced brain targeting in schizophrenia: in vitro and ex vivo evaluation

Robust securable economic operation of grid-connected renewable integrated energy system with hydrogen storage and electric and fuel cell vehicles

Differential expression genes in the eye-transcriptomes of the riverine and marine grass puffer (Takifugu niphobles)

Rifaximin improves anxiety- and depression-like behaviors in rats by regulating NPY in the colon and mPFC

Fuzzy logic and deep learning approach for automated white blood cell detection and classification via multi-CNN feature fusion

A novel approach to design compact, wideband, and high-gain circularly polarized MIMO antenna using dual-polarized elements and hybrid couplers

Tailoring π--d Magnetic Interactions in Metallated Porphyrin Nanotapes

Abstract Molecular assemblies based on porphyrins (Pors), specifically Por nanotapes (NTs) containing magnetic metal ions, offer a versatile platform to explore magnetic interactions arising from the electronic interplay between ‐conjugated ligands and transition metal d ‐orbitals. Using on‐surface synthesis under ultra‐high vacuum, we synthesized ‐extended PorNTs of different lengths incorporating magnetic metal ions such as Fe and Co on Au(111). We resolved their atomic structure using scanning tunneling microscopy (STM) and non‐contact atomic force microscopy (nc‐AFM). Differential conductance () measurements, interpreted by extensive density functional theory calculations and theoretical modeling, reveal two distinct magnetic behaviors for the Fe‐ and Co‐based systems. In FePorNTs, the magnetic interactions are dominated by strong Fe–ligand ferromagnetic coupling and weak antiferromagnetic Fe–Fe coupling. By contrast, CoPorNTs exhibit stronger Co–Co antiferromagnetic exchange and weaker Co–ligand coupling, with Kondo screening evident at the ligand sites. Our findings underscore the profound influence of metal centers, ligands, and substrate interactions on the magnetic and electronic properties of PorNTs, establishing these assemblies as interesting building blocks for low‐dimensional magnetism and future spintronic or quantum‐material applications.

L-ascorbate prevents non-alcoholic steatohepatitis-based hepatocarcinogenesis in Sod1/Prdx4 double-knockout mice

Colloidal Phase Control in Plasmonic Metal Oxide Nanocrystals via Competitive Metal–Ligand Equilibria

Abstract Colloidal nanocrystal gels offer tunable optical properties governed by both the nature of the building blocks and their spatial arrangement. When assembled via reversible molecular linkers, their phase behavior and structure are primarily dictated by bond strength and lability. However, precise control over these interactions remains a significant synthetic challenge and is often system‐specific. Here, we present a simple, broadly tunable linking strategy that modulates nanocrystal phase behavior and assembly structure by leveraging competitive metal–ligand equilibria. We achieve programmable control over gelation temperature and network structure by tuning competitive metal–terpyridine and metal–halide equilibria in terpyridine‐functionalized tin‐doped indium oxide (ITO) nanocrystals, governed by metal and halide identity, concentration, and temperature, enabling wide‐range infrared optical modulation. Combined kinetic Monte Carlo and optical simulations reveal that weaker, more labile links facilitate particle crawling, leading to denser gel structures with enhanced plasmon coupling. This strategy eliminates the need for complex ligand or linker design and establishes competitive coordination chemistry as a versatile platform for engineering dynamic, stimuli‐responsive colloidal assemblies.

Low power and high-speed quadrate node upset tolerant latch design using CNTFET

A hybrid SERWI ensemble model for crop yield prediction using an inverse RMSE weighting strategy

Abstract Accurate crop yield prediction is critical for informed agricultural planning, food distribution, and policy formulation. Traditional statistical models often fail to capture the nonlinear and temporal dynamics inherent in crop yield data. This Study introduces a novel hybrid Separate Evaluation of Regression models with Weighted Integration (SERWI) ensemble model, a novel hybrid ensemble model that integrates three machine learning algorithms: Long Short-Term Memory (LSTM) networks, Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). These base learners combine using a dynamic inverse RMSE-based weighting strategy that adaptively assigns higher weights to models exhibiting superior validation performance. The model is trained and evaluated using a comprehensive multi-decadal dataset sourced from the Season and Crop Report 2023–24 published by the Government of Tamil Nadu, which includes historical data on cultivated area, production, and yield of principal food grains, specifically focusing on foodgrains crop. Additionally, a detailed comparative analysis is performed against several individual models and ensemble combinations, including LSTM, SVR, XGBoost, Random Forest Regressor (RFR), Gaussian Process, and hybrid pairs such as LSTM plus SVR, SVR plus XGBoost, and LSTM plus RFR. SERWI outperformed the evaluated baseline models, achieving an RMSE of 70.16, MSE of 4923.07, MAE of 47.93, and R² of 0.9918 on the test set. These results indicate strong predictive performance and potential scalability for practical agricultural yield forecasting.