PWSC: a novel clustering method based on polynomial weight-adjusted sparse clustering for sparse biomedical data and its application in cancer subtyping.

BACKGROUND: Clustering analysis is widely used to interpret biomedical data and uncover new knowledge and patterns. However, conventional clustering methods are not effective when dealing with sparse biomedical data. To overcome this limitation, we propose a hierarchical clustering method called polynomial weight-adjusted sparse clustering (PWSC). RESULTS: The PWSC algorithm adjusts feature weights using a polynomial function, redefines the distances between samples, and performs hierarchical clustering analysis based on these adjusted distances. Additionally, we incorporate a consensus clustering approach to determine the optimal number of classifications. This consensus approach utilizes relative change in the cumulative distribution function to identify the best number of clusters, resulting in more stable clustering results. Leveraging the PWSC algorithm, we successfully classified a cohort of gastric cancer patients, enabling categorization of patients carrying different types of altered genes. Further evaluation using Entropy showed a significant improvement (p = 2.905e-05), while using the Calinski-Harabasz index demonstrates a remarkable 100% improvement in the quality of the best classification compared to conventional algorithms. Similarly, significantly increased entropy (p = 0.0336) and comparable CHI, were observed when classifying another colorectal cancer cohort with microbial abundance. The above attempts in cancer subtyping demonstrate that PWSC is highly applicable to different types of biomedical data. To facilitate its application, we have developed a user-friendly tool that implements the PWSC algorithm, which canbe accessed at http://pwsc.aiyimed.com/ . CONCLUSIONS: PWSC addresses the limitations of conventional approaches when clustering sparse biomedical data. By adjusting feature weights and employing consensus clustering, we achieve improved clustering results compared to conventional methods. The PWSC algorithm provides a valuable tool for researchers in the field, enabling more accurate and stable clustering analysis. Its application can enhance our understanding of complex biological systems and contribute to advancements in various biomedical disciplines.

Molecular Geometry-Directed Self-Recognition in the Self-Assembly of Giant Amphiphiles.

Three sets of polyoxometalate (POM)-based amphiphilic hybrid macromolecules with different rigidity in their organic tails are used as models to understand the effect of molecular rigidity on their possible self-recognition feature during self-assembly processes. Self-recognition is achieved in the mixed solution of two structurally similar, sphere-rigid T-shape-linked oligofluorene(TOF4 ) rod amphiphiles, with the hydrophilic clusters being Anderson (Anderson-TOF4 ) and Dawson (Dawson-TOF4 ), respectively. Anderson-TOF4 is observed to self-assemble into onion-like multilayer structures and Dawson-TOF4 forms multilayer vesicles. The self-assembly is controlled by the interdigitation of hydrophobic rods and the counterion-mediated attraction among charged hydrophilic inorganic clusters. When the hydrophobic blocks are less rigid, e.g., partially rigid polystyrene and fully flexible alkyl chains, self-recognition is not observed, attributing to the flexible conformation of hydrophobic molecules in the solvophobic domain. This study reveals that the self-recognition among amphiphiles can be achieved by the geometrical limitation of the supramolecular structure due to the rigidity of solvophobic domains.

Highly Recyclable and Tough Elastic Vitrimers from a Defined Polydimethylsiloxane Network.

Despite intensive research on sustainable elastomers, achieving elastic vitrimers with significantly improved mechanical properties and recyclability remains a scientific challenge. Herein, inspired by the classical elasticity theory, we present a design principle for ultra-tough and highly recyclable elastic vitrimers with a defined network constructed by chemically crosslinking the pre-synthesized disulfide-containing polydimethylsiloxane (PDMS) chains with tetra-arm polyethylene glycol (PEG). The defined network is achieved by the reduced dangling short chains and the relatively uniform molecular weight of network strands. Such elastic vitrimers with the defined network, i.e., PDMS-disulfide-D, exhibit significantly improved mechanical performance than random analogous, previously reported PDMS vitrimers, and even commercial silicone-based thermosets. Moreover, unlike the vitrimers with random network that show obvious loss in mechanical properties after recycling, those with the defined network enable excellent thermal recyclability. The PDMS-disulfide-D also deliver comparable electrochemical signals if utilized as substrates for electromyography sensors after the recycling. The multiple relaxation processes are revealed via a unique physical approach. Multiple techniques are also applied to unravel the microscopic mechanism of the excellent mechanical performance and recyclability of such defined network.

Accurate Determination of the Quantity and Spatial Distribution of Counterions around a Spherical Macroion.

The accurate distribution of countercations (Rb+ and Sr2+ ) around a rigid, spherical, 2.9-nm size polyoxometalate cluster, {Mo132 }42- , is determined by anomalous small-angle X-ray scattering. Both Rb+ and Sr2+ ions lead to shorter diffuse lengths for {Mo132 } than prediction. Most Rb+ ions are closely associated with {Mo132 } by staying near the skeleton of {Mo132 } or in the Stern layer, whereas more Sr2+ ions loosely associate with {Mo132 } in the diffuse layer. The stronger affinity of Rb+ ions towards {Mo132 } than that of Sr2+ ions explains the anomalous lower critical coagulation concentration of {Mo132 } with Rb+ compared to Sr2+ . The anomalous behavior of {Mo132 } can be attributed to majority of negative charges being located at the inner surface of its cavity. The longer anion-cation distance weakens the Coulomb interaction, making the enthalpy change owing to the breakage of hydration layers of cations more important in regulating the counterion-{Mo132 } interaction.

Synthesis, Assembly, and Sizing of Neutral, Lanthanide Substituted Molybdenum Blue Wheels {Mo90Ln10}.

Polyoxometalate molybdenum blue (MB) complexes typically exist as discrete multianionic clusters and are composed of repeating Mo building units. MB wheels such as {Mo176} and {Mo154} are made from pentagon-centered {Mo8} building blocks joined by equal number of {Mo1} units as loin, and {Mo2} dimer units as skirt along the ring edge, with the ring sizes of the MB wheels modulated by the {Mo2} units. Herein we report a new class of contracted lanthanide-doped MB structures that have replaced all the {Mo2} units with lanthanide ions on the inner rim, giving the general formula {Mo90Ln10}. We show three examples of this new decameric {Mo90Ln10} (Ln = La, Ce, and Pr) framework synthesized by high temperature reduction and demonstrate that later Ln ions result in {Mo92Ln9} (Ln = Nd, Sm), conserving one {Mo2} linker unit in its structure, as a consequence of the lanthanide contraction. Remarkably the {Mo90Ln10} compounds are the first examples of charge-neutral molybdate wheels as confirmed by BVS, solubility experiments, and redox titrations. We detail our full synthetic optimization for the isolation of these clusters and complete characterization by X-ray, TGA, UV-vis, and ICP studies. Finally, we show that this fine-tuned self-assembly process can be utilized to selectively enrich Ln-MB wheels for effective separation of lanthanides.

Enhanced Macroanion Recognition of Superchaotropic Keggin Clusters Achieved by Synergy of Anion-π and Anion-Cation Interactions.

Keggin clusters are the most widely used polyoxometalate building blocks for the construction advanced materials, but effective methods for precisely recognizing the isostructural analogues of Keggins are still limited. In this study we employed the zwitterionic molecule 4,4'-dipyridyl N,N'-dioxide as a recognition receptor to specifically bind to the three Keggin analogues PW12 O40 3- , PMo12 O40 3- , and SiW12 O40 4- , which separately co-assembled into three different types of spherical charged colloids of different sizes. The recognition phenomena were confirmed by electrochemical methods and their crystallization behavior. Compared with solely anion-cation interaction-driven systems, the synergism with the anion-π interactions between the superchaotropic Keggins and the electron-deficient pyridine rings is believed to enhance the recognition. This observation is intriguing as the long-range solution assembly of Keggins is mainly driven by short-range anion-π interactions. Our results show that the little-noticed hydration shell of Keggins is significantly influenced by the superchaotropic effect, leading to differentiated binding affinity to the receptors and more obvious recognition phenomena between tungsten/molybdenum Keggin analogues.

Co-ion Effects in the Self-Assembly of Macroions: From Co-ions to Co-macroions and to the Unique Feature of Self-Recognition.

Macroions, as soluble ions with a size on the nanometer scale, show unique solution behavior different from those of simple ions and large colloidal suspensions. In macroionic solutions, the counterions are known to be important and well-explored. However, the role of co-ions (ions carrying the same type of charge as the macroions) is often ignored. Here, through experimental and simulation studies, we demonstrate the role of co-ions as a function of co-ion size on their interaction with the macroions (using {Mo72Fe30} and {SrPd12} as models) and the related self-assembly into blackberry-type structures in dilute solutions. Several regimes of unique co-ion effects are clearly identified: small ions (halides, oxoacid ions), subnanometer-scaled bulky ions (lacunary Keggin and dodecaborate ions), and those with sizes comparable to the macroions. Small co-ions have no observable effect on the self-assembly of fully hydrophilic {Mo72Fe30}, while due to hydrophobic interaction and intermolecular hydrogen bonds, the small co-ions show influences on the self-assembly of hydrophobic {SrPd12}. Subnanometer ions, a.k.a. "superchaotropic ions", are still too small to assemble into a blackberry by themselves, but they can coassemble with the macroions, showing a strong interaction with the macroionic system. When the co-ion size is comparable to that of the macroions, they assemble independently instead of assembling with the macroions, leading to the previously reported unique self-recognition phenomenon for macroions.

Unraveling Chiral Selection in the Self-assembly of Chiral Fullerene Macroions: Effects of Small Chiral Components Including Counterions, Co-ions, or Neutral Molecules.

Lactic acid-functionalized chiral fullerene (C60) molecules are used as models to understand chiral selection in macroionic solutions involving chiral macroions, chiral counterions, and/or chiral co-ions. With the addition of Zn2+ cations, the C60 macroions exhibit slow self-assembly behavior into hollow, spherical, blackberry-type structures, as confirmed by laser light scattering (LLS), transmission electron microscopy (TEM), and atomic force microscopy (AFM) techniques. Chiral counterions with high charge density show no selection to the chirality of AC60 macroions (LAC60 and DAC60) during their self-assembly process, while obvious chiral discrimination between the assemblies of LAC60 and DAC60 is observed when chiral counterions with low charge density are present. Compared with chiral counterions, chiral co-ions show weaker effects on chiral selection with larger amounts needed to trigger the chiral discrimination between LAC60 and DAC60. However, they can induce a higher degree of discrimination when abundant chiral co-ions are present in solution. Furthermore, the self-assembly of chiral AC60 macroions is fully suppressed by adding significant amounts of neutral molecules with opposite chirality. Thermodynamic parameters from isothermal titration calorimetry (ITC) reveal that chiral selection is controlled by the ion pairing and the destruction of solvent shells between ions, and meanwhile originates from the delicate balance between electrostatic interaction and molecular chirality.

Unraveling the Effects of Cobalt on Crystal Growth and Solution Behavior of Nb6P2W12-based Dimeric Clusters.

We report the synthesis, characterization, and solution self-assembly of plenary Nb6P2W12-based transition-metal substituted polyoxometalate, which is obtained by simply adding transition metals (Co2+) into aqueous solution containing cluster [(NbO2)6P2W12O56]12-, which is obtained by an in situ synthetic method. The incorporation of Co2+ ions significantly affects the crystal structure, resulting in the formation of a 1D chain-like crystal and the first example of a niobotungstate-based cobalt derivative cluster. The behavior and stability of this cluster in solution are confirmed by time-resolved static light scattering, dynamic light scattering, small-angle X-ray scattering, and electrospray mass spectrometry studies.

Magnifying the Structural Components of Biomembranes: A Prototype for the Study of the Self-Assembly of Giant Lipids.

How biomembranes are self-organized to perform their functions remains a pivotal issue in biological and chemical science. Understanding the self-assembly principles of lipid-like molecules hence becomes crucial. Herein, we report the mesostructural evolution of amphiphilic sphere-rod conjugates (giant lipids), and study the roles of geometric parameters (head-tail ratio and cross-sectional area) during this course. As a prototype system, giant lipids resemble natural lipidic molecules by capturing their essential features. The self-assembly behavior of two categories of giant lipids (I-shape and T-shape, a total of 8 molecules) is demonstrated. A rich variety of mesostructures is constructed in solution state and their molecular packing models are rationally understood. Giant lipids recast the phase behavior of natural lipids to a certain degree and the abundant self-assembled morphologies reveal distinct physiochemical behaviors when geometric parameters deviate from natural analogues.

Competition and Cooperation among Different Attractive Forces in Solutions of Inorganic-Organic Hybrids Containing Macroionic Clusters.

Hybrids composed of nanoscale inorganic clusters and organic ligands are ideal models for understanding the different attractive forces during the self-assembly processes of complex macromolecules in solution. The counterion-mediated attraction induced by electrostatic interaction from the large, hydrophilic macroionic clusters can compete or cooperate with other types of attractive forces such as hydrophobic interactions, hydrogen bonding, π-π stacking, and cation-π interactions from the organic ligands, consequently determining the solution behaviors of the hybrid molecules including their self-assembly process and the final supramolecular structures. The incorporation of organic ligands also leads to interesting responsive behaviors to external stimuli. Through the manipulation of the hybrid composition, architecture, topology, and solution conditions (e.g., solvent polarity, pH, and temperature), versatile self-assembled morphologies can be achieved, providing new scientific opportunities and potential applications.

Self-Assembly of Polyoxometalate-Peptide Hybrids in Solution: Elucidating the Contributions of Multiple Possible Driving Forces.

Incorporating the building blocks of nature (e.g., peptides and DNA) into inorganic polyoxometalate (POM) clusters is a promising approach to improve the compatibilities of POMs in biological fields. To extend their biological applications, it is necessary to understand the importance of different non-covalent interactions during self-organization. A series of Anderson POM-peptide hybrids have been used as a simple model to demonstrate the role of different interactions in POM-peptide (biomolecules) systems. Regardless of peptide chain length, these hybrids follow similar solution behaviors, forming hollow, spherical supramolecular structures in acetonitrile/water mixed solvents. The incorporation of peptide tails introduces interesting stimuli-responsive properties to temperature, hybrid concentration, solvent polarity and ionic strength. Unlike the typical bilayer amphiphilic vesicles, they are found to follow the blackberry-type assemblies of hydrophilic macroions, which are regulated by electrostatic interaction and hydrogen bonding. The formation of electrostatic assemblies before the supramolecular formation is confirmed by ion-mobility mass spectrometry (IMS-MS).

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data.

Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future.

A Long Time-Series Radiometric Normalization Method for Landsat Images.

Radiometric normalization attempts to normalize the radiomimetic distortion caused by non-land surface-related factors, for example, different atmospheric conditions at image acquisition time and sensor factors, and to improve the radiometric consistency between remote sensing images. Using a remote sensing image and a reference image as a pair is a traditional method of performing radiometric normalization. However, when applied to the radiometric normalization of long time-series of images, this method has two deficiencies: first, different pseudo-invariant features (PIFs)-radiometric characteristics of which do not change with time-are extracted in different pairs of images; and second, when processing an image based on a reference, we can minimize the residual between them, but the residual between temporally adjacent images may induce steep increases and decreases, which may conceal the information contained in the time-series indicators, such as vegetative index. To overcome these two problems, we propose an optimization strategy for radiometric normalization of long time-series of remote sensing images. First, the time-series gray-scale values for a pixel in the near-infrared band are sorted in ascending order and segmented into different parts. Second, the outliers and inliers of the time-series observation are determined using a modified Inflexion Based Cloud Detection (IBCD) method. Third, the variation amplitudes of the PIFs are smaller than for vegetation but larger than for water, and accordingly the PIFs are identified. Last, a novel optimization strategy aimed at minimizing the correction residual between the image to be processed and the images processed previously is adopted to determine the radiometric normalization sequence. Time-series images from the Thematic Mapper onboard Landsat 5 for Hangzhou City are selected for the experiments, and the results suggest that our method can effectively eliminate the radiometric distortion and preserve the variation of vegetation in the time-series of images. Smoother time-series profiles of gray-scale values and uniform root mean square error distributions can be obtained compared with those of the traditional method, which indicates that our method can obtain better radiometric consistency and normalization performance.

Effect of Cation-π Interaction on Macroionic Self-Assembly.

A series of rod-shaped polyoxometalates (POMs) [Bu4 N]7 [Mo6 O18 NC(CH2 O)3 MnMo6 O18 (OCH2 )3 CNMo6 O18 ] and [Bu4 N]7 [ArNMo6 O17 NC(CH2 O)3 MnMo6 O18 (OCH2 )3 CNMo6 O17 NAr] (Ar=2,6-dimethylphenyl, naphthyl and 1-methylnaphthyl) were chosen to study the effects of cation-π interaction on macroionic self-assembly. Diffusion ordered spectroscopy (DOSY) and isothermal titration calorimetry (ITC) techniques show that the binding affinity between the POMs and Zn2+ ions is enhanced significantly after grafting aromatic groups onto the clusters, leading to the effective replacement of tetrabutylammonium counterions (TBAs) upon the addition of ZnCl2 . The incorporation of aromatic groups results in the significant contribution of cation-π interaction to the self-assembly, as confirmed by the opposite trend of assembly size vs. ionic strength when compared with those without aromatic groups. The small difference between two aromatic groups toward the Zn2+ ions is amplified after combining with the clusters, which consequently triggers the self-recognition behavior between two highly similar macroanions.

Strong Co-Ion Effect via Cation-π Interaction on the Self-Assembly of Metal-Organic Cationic Macrocycles.

The predesigned metal-organic macrocycle Zn3QDB3(NO3)4 (Zn-QDB) was observed to self-assemble into a hollow, spherical, single-layered "blackberry"-type structure. The self-assembly behaviors of the Zn-QDB are significantly influenced by additional small ions. Specifically, the cations exhibit strong co-ion effects on the interaction between cationic macrocycles which are different from the previously reported co-ion effects of simple anions on anionic polyoxometalates. This unusual phenomenon is due to the unique cation-π interaction between small cations and electron-rich cavity of Zn-QDB, as confirmed by UV-vis, 1H NMR, and fluorescence spectra. The variation of hydrodynamic radius (Rh) of assemblies with the changes of solution ionic strength and the type of cations reveals the competition between counterion-mediated attraction and cation-π interaction during the self-assembly process. Furthermore, the cooperativity of cation-π interaction and π-π stacking play a vital role in enhancing the stability of the supramolecular structure.

FATAL HONEY POISONING IN SOUTHWEST CHINA: A CASE SERIES OF 31 CASES.

Honey poisoning cases occur in southwestern China. In this case series, we attempted to determine the symptoms and causes of honey poisoning from 2007 to 2012 in southwestern China. We also conducted a quantitative melissopalynological analysis of honey samples. During the study period, 31 honey poisoning cases occurred in the study location, all during July to August. All the cases occurred after consuming at least 100 grams of honey. The most frequent symptoms were nausea and vomiting (100%), abdominal pain (90.3%), diarrhea (74.2%), palpitations (61.3%), dizziness (54.8%), chest congestion (48.4%) and dyspnea (48.4%). Severe cases developed oliguria/anuria, twitch, hematuria, ecchymosis or hematochezia. The median time from ingestion to onset of symptoms was 29 hours. Eight patients died (mortality rate: 25.8%). The pollen of Tripterygium hypoglaucum (a plant with poisonous nectar and pollen) was detected in 22 of 29 honey samples examined (75.9%). The results of pollen analysis were consistent with the clinical findings of previous cases. T. hypoglaucum appears to be the cause of honey poisoning in southwestern China. Honey poisoning should be included in the differential diagnosis of patients who consume honey in this region and develop symptoms of food poisoning.

Modification of the Solution Behavior of Pd12 L24 Metal-Organic Nanocages via PEGylation.

The dilute solutions behaviors of Pd12 L24 metal-organic nanocage and its two PEGylated derivatives are explored. The basic nanocages can self-assemble into vesicle-like blackberry structures in polar solvents via counterion-mediated attraction, whereas the PEGylated nanocages always stay as discrete ions under the same conditions, demonstrating that the PEGylation can improve the stability of the single nanocages. In addition, larger nanocages are found to self-assemble in less polar solvents.

Ductile adhesive elastomers with force-triggered ultra-high adhesion strength.

Elastomers play a vital role in many forthcoming advanced technologies in which their adhesive properties determine materials' interface performance. Despite great success in improving the adhesive properties of elastomers, permanent adhesives tend to stick to the surfaces prematurely or result in poor contact depending on the installation method. Thus, elastomers with on-demand adhesion that is not limited to being triggered by UV light or heat, which may not be practical for scenarios that do not allow an additional external source, provide a solution to various challenges in conventional adhesive elastomers. Herein, we report a novel, ready-to-use, ultra high-strength, ductile adhesive elastomer with an on-demand adhesion feature that can be easily triggered by a compression force. The precursor is mainly composed of a capsule-separated, two-component curing system. After a force-trigger and curing process, the ductile adhesive elastomer exhibits a peel strength and a lap shear strength of 1.2 × 104 N m-1 and 7.8 × 103 kPa, respectively, which exceed the reported values for advanced ductile adhesive elastomers. The ultra-high adhesion force is attributed to the excellent surface contact of the liquid-like precursor and to the high elastic modulus of the cured elastomer that is reinforced by a two-phase design. Incorporation of such on-demand adhesion into an elastomer enables a controlled delay between installation and curing so that these can take place under their individual ideal conditions, effectively reducing the energy cost, preventing failures, and improving installation processes.

Optimization of FFPE preparation and identification of gene attributes associated with RNA degradation.

Formalin-fixed paraffin-embedded (FFPE) tissues are widely available specimens for clinical studies. However, RNA degradation in FFPE tissues often restricts their utility. In this study, we determined optimal FFPE preparation conditions, including tissue ischemia at 4°C (<48 h) or 25°C for a short time (0.5 h), 48-h fixation at 25°C and sampling from FFPE scrolls instead of sections. Notably, we observed an increase in intronic reads and a significant change in gene rank based on expression level in the FFPE as opposed to fresh-frozen (FF) samples. Additionally, we found that more reads were mapped to genes associated with chemical stimulus in FFPE samples. Furthermore, we demonstrated that more degraded genes in FFPE samples were enriched in genes with short transcripts and high free energy. Besides, we found 40 housekeeping genes exhibited stable expression in FF and FFPE samples across various tissues. Moreover, our study showed that FFPE samples yielded comparable results to FF samples in dimensionality reduction and pathway analyses between case and control samples. Our study established the optimal conditions for FFPE preparation and identified gene attributes associated with degradation, which would provide useful clues for the utility of FFPE tissues in clinical practice and research.