Geospatial insights into Alphonso mango cultivation: a comprehensive land suitability study in the coastal belt of Maharashtra, India.

Land suitability assessment is integral to the advancement of precision agriculture. This inquiry is focused on identifying optimal regions for cultivating Alphonso mango in the coastal belt of Maharashtra, spanning across Palghar, Raigad, Thane, Ratnagiri, and Sindhudurg districts. Employing a GIS-based Analytic Hierarchy Process (AHP) methodology, 10 crucial parameters have been considered, encompassing climatic, physical, and chemical soil characteristics: cation exchange capacity, organic carbon, slope, rainfall, soil pH, soil texture, mean annual soil temperature, base saturation, soil drainage, and soil depth. Weights are assigned to these parameters based on expert opinions and existing literature to determine their significance in developing a soil suitability map. The study reveals distinct land suitability zones for Alphonso mango cultivation. The land suitability map designates 25.78% of the study area as highly suitable, while 9.18% is considered unsuitable for Alphonso mango cultivation. To validate the study, the Receiver Operating Characteristic (ROC) curve has been employed, indicating an 83% approval rate for the reliability and performance of the soil suitability. The results categorise soil suitability classes, providing valuable insights for farmers and agricultural planners to make informed decisions regarding Alphonso mango cultivation in similar geoenvironmental regions.

Polyoxometalate-Polymer Directed Macromolecular Architectonics of Silver Nanoparticles as Effective Antimicrobials.

A novel inorganic-organic-inorganic ternary bioactive material formulated on antimicrobial peptide-based polymer has been reported. Supramolecular approach has been employed to incorporate molecularly crowded tyrosine-based polymer stabilized silver nanoparticles into membrane bound vesicles exploiting polyoxometalate-triggered surface templating strategy. Utilizing the covalent reversible addition fragmentation chain transfer (RAFT) polymerization and exploiting templated supramolecular architectonics at biopolymer interface, the bioactive ternary polymeric nanohybrids have been designed against Shigellosis leveraging the antibacterial activities of silver nanoparticle, cationic amphiphilic tyrosine polymer and inorganic polyoxometalate. The detail investigation against Shigella flexneri 2a cell line demonstrates that the collaborative mechanism of the ternary hybrid composite enhances the bactericidal activity in comparison to only polyoxometalate and polymer stabilized silver nanoparticle with an altered mechanism of action which is established via detailed biological analysis.

The Importance of Spin-Polarized Charge Reorganization in the Catalytic Activity of D-Glucose Oxidase.

The front cover artwork is provided by Prof. Ron Naaman's group at the Weizmann Institute of Science. The image shows that direct electron transfer through GOx is governed by electron spins, which result from the chiral-induced spin selectivity (CISS) effect. Read the full text of the Research Article at 10.1002/cphc.202400033.

Insights into the Mechanism of Chiral-Induced Spin Selectivity: The Effect of Magnetic Field Direction and Temperature.

Chiral oligopeptide monolayers are adsorbed on a ferromagnetic surface and their magnetoresistance is measured as a function of the angle between the magnetization of the ferromagnet and the surface normal. These measurements are conducted as a function of temperature for both enantiomers. The angle dependence is found to follow a changing trend with a period of 360°. Quantum simulations reveal that the angular distribution can be obtained only if the monolayer has significant effective spin orbit coupling (SOC), that includes contribution from the vibrations. The model shows that SOC only in the leads cannot reproduce the observed angular dependence. The simulation can reproduce the experiments if it included electron-phonon interactions and dissipation.

The Importance of Spin-Polarized Charge Reorganization in the Catalytic Activity of D-Glucose Oxidase.

The reaction of D-glucose oxidase (GOx) with D- and L-glucose was investigated using confocal fluorescence microscopy and Hall voltage measurements, after the enzyme was adsorbed as a monolayer. By adsorbing the enzyme on a ferromagnetic substrate, we verified that the reaction is spin dependent. This conclusion was supported by monitoring the reaction when the enzyme is adsorbed on a Hall device that does not contain any magnetic elements. The spin dependence is consistent with the chiral-induced spin selectivity (CISS) effect; it can be explained by the improved fidelity of the electron transfer process through the chiral enzyme due to the coupling of the linear momentum of the electrons and their spin. Since the reaction studied often serve as a model system for enzymatic activity, the results may suggest the general importance of the spin-dependent electron transfer in bio-chemical processes.

Monitoring of Visible Particles in Parenteral Products by Manual Visual Inspection-Reassessing Size Threshold and Other Particle Characteristics that Define Particle Visibility.

Visible particles are a critical quality attribute for parenteral products and must be monitored. A carefully designed, executed, and controlled drug product manufacturing process including a final 100 % visual inspection and appropriate end-product controls ensures that visible particles are consistently minimized and demonstrates that the injectable DP is practically free from visible particles. Visual inspection, albeit appearing as a simple analytical procedure, requires several technical and operational controls to ensure adequate performance. To gather new data on particle visibility and shed light on this decade-old challenge, a multi-company blinded visual inspection threshold study was conducted. A major goal of the study was visual assessment of several particle types of different sizes in small volume vials, as a challenging configuration for visual inspection, across 9 biopharmaceutical companies in order to determine the visibility limit. The study results provide key insights into limitations and challenges of visual inspection, namely, no universal visibility limit can be applied to all particle types as the detectability varies with particle type, number, and size. The study findings underscore the necessity of setting realistic expectations on size-based visibility limits in visual inspection, robust procedures for analyst training and qualification, and harmonization of guidelines globally.

Highly Conductive Topologically Chiral Molecular Knots as Efficient Spin Filters.

Knot-like structures were found to have interesting magnetic properties in condensed matter physics. Herein, we report on topologically chiral molecular knots as efficient spintronic chiral material. The discovery of the chiral-induced spin selectivity (CISS) effect opens the possibility of manipulating the spin orientation with soft materials at room temperature and eliminating the need for a ferromagnetic electrode. In the chiral molecular trefoil knot, there are no stereogenic carbon atoms, and chirality results from the spatial arrangements of crossings in the trefoil knot structures. The molecules show a very high spin polarization of nearly 90%, a conductivity that is higher by about 2 orders of magnitude compared with that of other chiral small molecules, and enhanced thermal stability. A plausible explanation for these special properties is provided, combined with model calculations, that supports the role of electron-electron interaction in these systems.

Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants.

The mortality rates of patients contracting the Omicron and Delta variants of COVID-19 are very high, and COVID-19 is the worst variant of COVID. Hence, our objective is to detect COVID-19 Omicron and Delta variants from lung CT-scan images. We designed a unique ensemble model that combines the CNN architecture of a deep neural network-Capsule Network (CapsNet)-and pre-trained architectures, i.e., VGG-16, DenseNet-121, and Inception-v3, to produce a reliable and robust model for diagnosing Omicron and Delta variant data. Despite the solo model's remarkable accuracy, it can often be difficult to accept its results. The ensemble model, on the other hand, operates according to the scientific tenet of combining the majority votes of various models. The adoption of the transfer learning model in our work is to benefit from previously learned parameters and lower data-hunger architecture. Likewise, CapsNet performs consistently regardless of positional changes, size changes, and changes in the orientation of the input image. The proposed ensemble model produced an accuracy of 99.93%, an AUC of 0.999 and a precision of 99.9%. Finally, the framework is deployed in a local cloud web application so that the diagnosis of these particular variants can be accomplished remotely.

Bacterial extracellular electron transfer components are spin selective.

Metal-reducing bacteria have adapted the ability to respire extracellular solid surfaces instead of soluble oxidants. This process requires an electron transport pathway that spans from the inner membrane, across the periplasm, through the outer membrane, and to an external surface. Multiheme cytochromes are the primary machinery for moving electrons through this pathway. Recent studies show that the chiral-induced spin selectivity (CISS) effect is observable in some of these proteins extracted from the model metal-reducing bacteria, Shewanella oneidensis MR-1. It was hypothesized that the CISS effect facilitates efficient electron transport in these proteins by coupling electron velocity to spin, thus reducing the probability of backscattering. However, these studies focused exclusively on the cell surface electron conduits, and thus, CISS has not been investigated in upstream electron transfer components such as the membrane-associated MtrA, or periplasmic proteins such as small tetraheme cytochrome (STC). By using conductive probe atomic force microscopy measurements of protein monolayers adsorbed onto ferromagnetic substrates, we show that electron transport is spin selective in both MtrA and STC. Moreover, we have determined the spin polarization of MtrA to be ∼77% and STC to be ∼35%. This disparity in spin polarizations could indicate that spin selectivity is length dependent in heme proteins, given that MtrA is approximately two times longer than STC. Most significantly, our study indicates that spin-dependent interactions affect the entire extracellular electron transport pathway.

Chirality Versus Symmetry: Electron's Spin Selectivity in Nonpolar Chiral Lead-Bromide Perovskites.

In the last decade, chirality-induced spin selectivity (CISS), the spin-selective electron transport through chiral molecules, has been described in a large range of materials, from insulators to superconductors. Because more experimental studies are desired for the theoretical understanding of the CISS effect, chiral metal-halide semiconductors may contribute to the field thanks to their chiroptical and spintronic properties. In this regard, this work uses new chiral organic cations S-HP1A and R-HP1A (HP1A = 2-hydroxy-propyl-1-ammonium) to prepare 2D chiral halide perovskites (HPs) which crystallize in the enantiomorphic space groups P43 21 2 and P41 21 2, respectively. The fourfold symmetry induces antiferroelectricity along the stacking axis which, combined to incomplete Rashba-like splitting in each individual 2D polar layer, results in rare spin textures in the band structure. As revealed by magnetic conductive-probe atomic force microscopy (AFM) measurements, these materials show CISS effect with partial spin polarization (SP; ±40-45%). This incomplete effect is efficient enough to drive a chiro-spintronic device as demonstrated by the fabrication of spin valve devices with magnetoresistance (MR) responses up to 250 K. Therefore, these stable lead-bromide HP materials not only represent interesting candidates for spintronic applications but also reveal the importance of polar symmetry-breaking topology for spin selectivity.

Spin-induced electron transmission through metal-organic chiral crystals.

Metal-organic Co(II)-phenylalanine crystals were studied and were found to possess magnetic properties and long-range spin transport. Magnetic measurements confirmed that in the crystals there are antiferromagnetic interactions between Co(II) and the lattice. The metal-organic crystals (MOCs) also present the chirality-induced spin selectivity (CISS) effect at room temperature. A long-range spin polarization is observed using a magnetic conductive-probe atomic force microscope. The spin polarization is found to be in the range of 35-45%.

A comprehensive strategy for the identification of biologics by liquid-chromatography-mass spectrometry for release testing in a regulated environment.

Identification (ID) testing is a regulatory requirement for biopharmaceutical manufacturing, requiring robust, GMP-qualified assays that can distinguish the therapeutic from any other in the facility. Liquid Chromatography-Mass Spectrometry (LC-MS) is a powerful analytical tool used to identify and characterize biologics. While routinely leveraged for characterization, LC-MS is relatively rare in Quality Control (QC) settings due to its perceived complexity and scarcity of MS-trained personnel. However, employing LC-MS for identification of drug products has many advantages versus conventional ID techniques, including but not limited to its high specificity, rapid turn-around time, and ease of method execution. In this work, we outline the development and implementation of a comprehensive LC-MS based ID strategy for biologics release testing. Two main workflows (WFs) were developed: i) WF1, a subunit-based assay measuring the molecular weight of the light chain (LC) and heavy chain (HC) of an antibody upon reduction, and ii) WF2, intact mass measurement of the biologic upon N-deglycosylation by PNGase F. The proposed strategy is shown to be applicable for over 40 diverse model biologics including monoclonal antibodies (mAbs), biobetters such as antibody prodrugs/afucosylated mAbs, fusion proteins, multi-specific antibodies, Fabs, and large peptides, all with excellent mass accuracy (error typically < 20 ppm) and precision. It requires a single-step sample preparation and a single click to run and process the data upon method setup. This strategy has been successfully implemented for release testing in GMP labs. Challenges and considerations for the establishment of QC-friendly methods are discussed. It is also shown that these methods can be applied to the ID of more analytically complex biotherapeutics, such as fixed-dose combination (FDC) and drug products co-formulated with trace-level additives.

Efficient Spin-Selective Electron Transport at Low Voltages of Thia-Bridged Triarylamine Hetero[4]helicenes Chemisorbed Monolayer.

The Chirality Induced Spin Selectivity (CISS) effect describes the capability of chiral molecules to act as spin filters discriminating flowing electrons according to their spin state. Within molecular spintronics, efforts are focused on developing chiral-molecule-based technologies to control the injection and coherence of spin-polarized currents. Herein, for this purpose, we study spin selectivity properties of a monolayer of a thioalkyl derivative of a thia-bridged triarylamine hetero[4]helicene chemisorbed on a gold surface. A stacked device assembled by embedding a monolayer of these molecules between ferromagnetic and diamagnetic electrodes exhibits asymmetric magnetoresistance with inversion of the signal according to the handedness of molecules, in line with the presence of the CISS effect. In addition, magnetically conductive atomic force microscopy reveals efficient electron spin filtering even at unusually low potentials. Our results demonstrate that thia[4]heterohelicenes represent key candidates for the development of chiral spintronic devices.

Surface-Induced Protein Aggregation and Particle Formation in Biologics: Current Understanding of Mechanisms, Detection and Mitigation Strategies.

Protein stability against aggregation is a major quality concern for the production of safe and effective biopharmaceuticals. Amongst the different drivers of protein aggregation, increasing evidence indicates that interactions between proteins and interfaces represent a major risk factor for the formation of protein aggregates in aqueous solutions. Potentially harmful surfaces relevant to biologics manufacturing and storage include air-water and silicone oil-water interfaces as well as materials from different processing units, storage containers, and delivery devices. The impact of some of these surfaces, for instance originating from impurities, can be difficult to predict and control. Moreover, aggregate formation may additionally be complicated by the simultaneous presence of interfacial, hydrodynamic and mechanical stresses, whose contributions may be difficult to deconvolute. As a consequence, it remains difficult to identify the key chemical and physical determinants and define appropriate analytical methods to monitor and predict protein instability at these interfaces. In this review, we first discuss the main mechanisms of surface-induced protein aggregation. We then review the types of contact materials identified as potentially harmful or detected as potential triggers of proteinaceous particle formation in formulations and discuss proposed mitigation strategies. Finally, we present current methods to probe surface-induced instabilities, which represent a starting point towards assays that can be implemented in early-stage screening and formulation development of biologics.

End-to-End Approach to Surfactant Selection, Risk Mitigation, and Control Strategies for Protein-Based Therapeutics.

A survey performed by the AAPS Drug Product Handling community revealed a general, mostly consensus, approach to the strategy for the selection of surfactant type and level for biopharmaceutical products. Discussing and building on the survey results, this article describes the common approach for surfactant selection and control strategy for protein-based therapeutics and focuses on key studies, common issues, mitigations, and rationale. Where relevant, each section is prefaced by survey responses from the 22 anonymized respondents. The article format consists of an overview of surfactant stabilization, followed by a strategy for the selection of surfactant level, and then discussions regarding risk identification, mitigation, and control strategy. Since surfactants that are commonly used in biologic formulations are known to undergo various forms of degradation, an effective control strategy for the chosen surfactant focuses on understanding and controlling the design space of the surfactant material attributes to ensure that the desired material quality is used consistently in DS/DP manufacturing. The material attributes of a surfactant added in the final DP formulation can influence DP performance (e.g., protein stability). Mitigation strategies are described that encompass risks from host cell proteins (HCP), DS/DP manufacturing processes, long-term storage, as well as during in-use conditions.

Geospatial assessment of agricultural drought vulnerability using integrated three-dimensional model in the upper Dwarakeshwar river basin in West Bengal, India.

The amount of agricultural drought vulnerability in an underdeveloped rain-fed agro-based economy at the local, regional, and national level is most prominent factor for measurement. The desiccation of rain in agricultural sector becomes apprehensive to intercontinental food supply chain. So, adequate investigation and development of sustainable agricultural methodology are key factors to sustain the food security of a territory. In this research, delineation of agricultural drought vulnerability (ADV) status has been carried out by multidimensional mixed-method index approach using remote sensing and geographic information system. An integrated three-dimensional model is utilized to enrich this study. The three indices of this model include exposure index (EI), sensitivity index (SI), and adaptive capacity index (ACI). The ACI has been constructed by combining the environmental adaptive capacity (EAC), social adaptive capacity (SAC), and economic adaptive capacity (EcAC) index. The 40 parameters for ADV modeling are picked up by analyzing meteorological, geo-environmental, social, and remote sensing data. There are six exposure parameters, seven sensitivity parameters, twelve environmental adaptive capacity parameters, six social adaptive capacity parameters, and nine economic adaptive capacity parameters. Each index has been computed by assigning the weights based on their relative importance by using the analytic hierarchy process (AHP) approach. Final results were classified into five vulnerability zones, e.g., very low, low, moderate, high, and very high covering an area 362.32 km2, 186.68 km2, 568.69 km2, 547.05 km2, and 266.89 km2 respectively. Results have been validated with long-term Aman paddy yield data (2004 to 2014) through the yield anomaly index (YAI). Finally, the model ADV is a good model fit (R square = 0.894) and all the relationships were found significant, when SI, EI, and ACI are considered its predictors. While SI (B = 0.391, p < 0.001) and EI (B = 0.223, p < 0.001) are positively associated with ADV, ACI is negatively associated with ADV (B = - 0.721, p < 0.001). This regional agricultural drought vulnerability model can be useful to identify drought-responsive areas and improve drought mitigation measures.

Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images.

Convolutional Neural Network (CNN) has been employed in classifying the COVID cases from the lungs' CT-Scan with promising quantifying metrics. However, SARS COVID-19 has been mutated, and we have many versions of the virus B.1.1.7, B.1.135, and P.1, hence there is a need for a more robust architecture that will classify the COVID positive patients from COVID negative patients with less training. We have developed a neural network based on the number of channels present in the images. The CNN architecture is developed in accordance with the number of the channels present in the dataset and are extracting the features separately from the channels present in the CT-Scan dataset. In the tower architecture, the first tower is dedicated for only the first channel present in the image; the second CNN tower is dedicated to the first and second channel feature maps, and finally the third channel takes account of all the feature maps from all three channels. We have used two datasets viz. one from Tongji Hospital, Wuhan, China and another SARS-CoV-2 dataset to train and evaluate our CNN architecture. The proposed model brought about an average accuracy of 99.4%, F1 score 0.988, and AUC 0.99.

Spin-induced asymmetry reaction-The formation of asymmetric carbon by electropolymerization.

We describe the spin polarization-induced chirogenic electropolymerization of achiral 2-vinylpyridine, which forms a layer of enantioenhanced isotactic polymer on the electrode. The product formed is enantioenriched in asymmetric carbon polymer. To confirm the chirality of the polymer film formed on the electrode, we also measured its electron spin polarization properties as a function of its thickness. Two methods were used: First, spin polarization was measured by applying magnetic contact atomic force microscopy, and second, magnetoresistance was assessed in a sandwich-like four-point contact structure. We observed high spin-selective electron transmission, even for a layer thickness of 120 nm. A correlation exists between the change in the circular dichroism signal and the change in the spin polarization, as a function of thickness. The spin-filtering efficiency increases with temperature.

Dual-detection approach for a charge variant analysis of monoclonal antibody combination products using imaged capillary isoelectric focusing.

The clinical benefits of treatments with a combination of two or more therapeutic monoclonal antibodies (mAbs) have emerged in recent years. Imaged capillary isoelectric focusing is a frequently used technology in the biopharmaceutical industry for charge variant analysis of protein therapeutics. However, with the wide concentration ranges of combination products, one component may fall within the linear detection range, whereas the other does not. Here, we report a novel methodology to explore charge variants of mAb mixtures using multiple detection techniques simultaneously. We use ultraviolet absorbance to monitor the charge variants of the high-concentration component and native fluorescence (FL) to monitor the variants of the low-concentration one. Charge variants of mixtures that span 40-fold in ratio differences can be accurately quantified with this approach. In contrast to the conventional methods, it is not necessary to prepare and analyze two samples at different concentrations and combine the results for combination product testing. Additionally, the use of FL detection enables the charge variant analysis of highly potent/low abundant mAbs in a mixture. This methodology is more quality-control friendly and efficient for the charge variant analysis of combination products with wide ratios.