Table4_Identification of Type 2 Diabetes Biomarkers From Mixed Single-Cell Sequencing Data With Feature Selection Methods.XLSX

Diabetes is the most common disease and a major threat to human health. Type 2 diabetes (T2D) makes up about 90% of all cases. With the development of high-throughput sequencing technologies, more and more fundamental pathogenesis of T2D at genetic and transcriptomic levels has been revealed. The recent single-cell sequencing can further reveal the cellular heterogenicity of complex diseases in an unprecedented way. With the expectation on the molecular essence of T2D across multiple cell types, we investigated the expression profiling of more than 1,600 single cells (949 cells from T2D patients and 651 cells from normal controls) and identified the differential expression profiling and characteristics at the transcriptomics level that can distinguish such two groups of cells at the single-cell level. The expression profile was analyzed by several machine learning algorithms, including Monte Carlo feature selection, support vector machine, and repeated incremental pruning to produce error reduction (RIPPER). On one hand, some T2D-associated genes (MTND4P24, MTND2P28, and LOC100128906) were discovered. On the other hand, we revealed novel potential pathogenic mechanisms in a rule manner. They are induced by newly recognized genes and neglected by traditional bulk sequencing techniques. Particularly, the newly identified T2D genes were shown to follow specific quantitative rules with diabetes prediction potentials, and such rules further indicated several potential functional crosstalks involved in T2D.</p

Table2_Identification of Type 2 Diabetes Biomarkers From Mixed Single-Cell Sequencing Data With Feature Selection Methods.XLSX

Diabetes is the most common disease and a major threat to human health. Type 2 diabetes (T2D) makes up about 90% of all cases. With the development of high-throughput sequencing technologies, more and more fundamental pathogenesis of T2D at genetic and transcriptomic levels has been revealed. The recent single-cell sequencing can further reveal the cellular heterogenicity of complex diseases in an unprecedented way. With the expectation on the molecular essence of T2D across multiple cell types, we investigated the expression profiling of more than 1,600 single cells (949 cells from T2D patients and 651 cells from normal controls) and identified the differential expression profiling and characteristics at the transcriptomics level that can distinguish such two groups of cells at the single-cell level. The expression profile was analyzed by several machine learning algorithms, including Monte Carlo feature selection, support vector machine, and repeated incremental pruning to produce error reduction (RIPPER). On one hand, some T2D-associated genes (MTND4P24, MTND2P28, and LOC100128906) were discovered. On the other hand, we revealed novel potential pathogenic mechanisms in a rule manner. They are induced by newly recognized genes and neglected by traditional bulk sequencing techniques. Particularly, the newly identified T2D genes were shown to follow specific quantitative rules with diabetes prediction potentials, and such rules further indicated several potential functional crosstalks involved in T2D.</p

Location, inventory and testing decisions in closed-loop supply chains: A multimedia company

Our partnering firm is a Chinese manufacturer of multimedia products that needs guidance developing its imminent Closed-Loop Supply Chain (CLSC). To study this problem, we take into account location, inventory, and testing decisions in a CLSC setting with stochastic demands of new and time-sensitive returned products. Our analysis pays particular attention to the different roles assigned to the reverse Distribution Centers (DCs) and how each option affects the optimal CLSC design. The roles considered are collection and consolidation, additional testing tasks, and direct shipments with no reverse DCs. The problem concerning our partnering firm is formulated as a scenario-based chance-constrained mixed-integer program and it is reformulated to a conic quadratic mixed-integer program that can be solved efficiently via commercial optimization packages. The completeness of the model proposed allows us to develop a decision support tool for the firm and to offer several useful managerial insights. These insights are inferred from our computational experiments using data from the Chinese firm and a second data set based on the U.S. geography. Particularly interesting insights are related to how changes in the reverse flows can impact the forward supply chain and the inventory dynamics concerning the joint DCs.</p

Table1_Identification of Type 2 Diabetes Biomarkers From Mixed Single-Cell Sequencing Data With Feature Selection Methods.XLSX

Diabetes is the most common disease and a major threat to human health. Type 2 diabetes (T2D) makes up about 90% of all cases. With the development of high-throughput sequencing technologies, more and more fundamental pathogenesis of T2D at genetic and transcriptomic levels has been revealed. The recent single-cell sequencing can further reveal the cellular heterogenicity of complex diseases in an unprecedented way. With the expectation on the molecular essence of T2D across multiple cell types, we investigated the expression profiling of more than 1,600 single cells (949 cells from T2D patients and 651 cells from normal controls) and identified the differential expression profiling and characteristics at the transcriptomics level that can distinguish such two groups of cells at the single-cell level. The expression profile was analyzed by several machine learning algorithms, including Monte Carlo feature selection, support vector machine, and repeated incremental pruning to produce error reduction (RIPPER). On one hand, some T2D-associated genes (MTND4P24, MTND2P28, and LOC100128906) were discovered. On the other hand, we revealed novel potential pathogenic mechanisms in a rule manner. They are induced by newly recognized genes and neglected by traditional bulk sequencing techniques. Particularly, the newly identified T2D genes were shown to follow specific quantitative rules with diabetes prediction potentials, and such rules further indicated several potential functional crosstalks involved in T2D.</p

Table3_Identification of Type 2 Diabetes Biomarkers From Mixed Single-Cell Sequencing Data With Feature Selection Methods.XLSX

Diabetes is the most common disease and a major threat to human health. Type 2 diabetes (T2D) makes up about 90% of all cases. With the development of high-throughput sequencing technologies, more and more fundamental pathogenesis of T2D at genetic and transcriptomic levels has been revealed. The recent single-cell sequencing can further reveal the cellular heterogenicity of complex diseases in an unprecedented way. With the expectation on the molecular essence of T2D across multiple cell types, we investigated the expression profiling of more than 1,600 single cells (949 cells from T2D patients and 651 cells from normal controls) and identified the differential expression profiling and characteristics at the transcriptomics level that can distinguish such two groups of cells at the single-cell level. The expression profile was analyzed by several machine learning algorithms, including Monte Carlo feature selection, support vector machine, and repeated incremental pruning to produce error reduction (RIPPER). On one hand, some T2D-associated genes (MTND4P24, MTND2P28, and LOC100128906) were discovered. On the other hand, we revealed novel potential pathogenic mechanisms in a rule manner. They are induced by newly recognized genes and neglected by traditional bulk sequencing techniques. Particularly, the newly identified T2D genes were shown to follow specific quantitative rules with diabetes prediction potentials, and such rules further indicated several potential functional crosstalks involved in T2D.</p

Graphene-like Molecules Based on Tetraphenylethene Oligomers: Synthesis, Characterization, and Applications

Graphene-like molecules were prepared by oxidative cyclodehydrogenation of tetraphenylethene­(TPE) oligomers using iron­(III) chloride as the catalyst under mild conditions. All the oxidized samples can be separated effectively from the stepwise ring-closing reaction that highly related to the reaction time. For example, the model compounds obtained from the stepwise cyclization reaction show a regular red-shift in UV/vis absorption and photoluminescence (PL) spectra. This result reveals that the molecular conjugation length will extend with the stepwise ring-closing reaction going on. Interestingly, we successfully obtained a series of colorful luminogens with blue, cyan, and green emission during this stepwise and accurate ring closing process. Cyclic voltammetry measurements taken give the corresponding band gap, which supports the results obtained from optical spectroscopy. For the strong intermolecular interaction, our graphene molecules can self-assemble to form a red-colored and hexagonal fiber. Furthermore, some molecules exhibit piezochromic luminescence. The PL emission of the molecules before and after oxidation can be dramatically quenched by picric acid through the electron transfer and/or energy transfer mechanism, enabling them to function as chemosensors for explosive detection. In addition, fluorescence cell imaging studies proved their potential biological application

Genomic context of the sponge candidates.

The upper bar chart shows the percentage for the different types of transcripts in the genome based on GENCODE and circBase, and their percentage within our sponge predictions are calculated after we assign annotations to the predicted sponge candidates. For each type of transcript, we calculate the percentage of their nucleotides under whole genome and annotated sponges. Then we can evaluate the enrichment via comparing the percent between sponges and whole genome. There is big overlap between PCGs and circRNAs, so we further divide them into “PCG not circRNA”, “circRNA not PCG” and “circRNA and PCG”. They refer to PCGs not overlapping with circRNAs, circRNAs not overlapping with PCGs, and PCGs overlapping with circRNAs, respectively. The lower bar chart shows the percentage of nucleotides located in intron, exon, 3’ UTR, and 5’ UTR for all annotated PCG sponge candidates. All percentages are calculated based on the number of nucleotides, excluding masked repeats, and are strand-sensitive.</p

Overall cluster size distribution of miRNA binding sites predicted by RIsearch2.

The plot shows the size distributions obtained for real and shuffled genomes when pooling the results for 2578 mature human miRNAs. For each miRNA, we used RIsearch2 to predict binding sites and clustered them using MCL with inflation factor 3.5.</p

Graphene-like Molecules Based on Tetraphenylethene Oligomers: Synthesis, Characterization, and Applications

Graphene-like molecules were prepared by oxidative cyclodehydrogenation of tetraphenylethene­(TPE) oligomers using iron­(III) chloride as the catalyst under mild conditions. All the oxidized samples can be separated effectively from the stepwise ring-closing reaction that highly related to the reaction time. For example, the model compounds obtained from the stepwise cyclization reaction show a regular red-shift in UV/vis absorption and photoluminescence (PL) spectra. This result reveals that the molecular conjugation length will extend with the stepwise ring-closing reaction going on. Interestingly, we successfully obtained a series of colorful luminogens with blue, cyan, and green emission during this stepwise and accurate ring closing process. Cyclic voltammetry measurements taken give the corresponding band gap, which supports the results obtained from optical spectroscopy. For the strong intermolecular interaction, our graphene molecules can self-assemble to form a red-colored and hexagonal fiber. Furthermore, some molecules exhibit piezochromic luminescence. The PL emission of the molecules before and after oxidation can be dramatically quenched by picric acid through the electron transfer and/or energy transfer mechanism, enabling them to function as chemosensors for explosive detection. In addition, fluorescence cell imaging studies proved their potential biological application

Flowchart of the analysis pipeline.

For each mature miRNA in miRBase v20, we ran RIsearch2 against both the real repeat-masked genome and a shuffled version to predict binding sites. We then used the Markov Cluster (MCL) algorithm to identify genomic clusters of binding sites and identified statistically significant clusters by comparing the results for the real and shuffled genomes. Finally, the significant clusters were further filtered by conservation and binding energy.</p