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The frequent use of chemotherapy to combat a range of malignancies can elicit severe cognitive dysfunction often referred to as "chemobrain," a condition that can persist long after the cessation of treatment in as many as 75% of survivors. Although cognitive health is a critical determinant of therapeutic outcome, chemobrain remains an unmet medical need that adversely affects quality of life in pediatric and adult cancer survivors. Using a rodent model of chemobrain, we showed that chronic cyclophosphamide treatment induced significant performance-based decrements on behavioral tasks designed to interrogate hippocampal and cortical function. Intrahippocampal transplantation of human neural stem cells resolved all cognitive impairments when animals were tested 1 month after the cessation of chemotherapy. In transplanted animals, grafted cells survived (8%) and differentiated along neuronal and astroglial lineages, where improved cognition was associated with reduced neuroinflammation and enhanced host dendritic arborization. Stem cell transplantation significantly reduced the number of activated microglia after cyclophosphamide treatment in the brain. Granule and pyramidal cell neurons within the dentate gyrus and CA1 subfields of the hippocampus exhibited significant reductions in dendritic complexity, spine density, and immature and mature spine types following chemotherapy, adverse effects that were eradicated by stem cell transplantation. Our findings provide the first evidence that cranial transplantation of stem cells can reverse the deleterious effects of chemobrain, through a trophic support mechanism involving the attenuation of neuroinflammation and the preservation host neuronal architecture.

We have observed that many computer-supported writing environments based on pedagogical strategies have only been designed to incorporate the cognitive aspects, but motivational aspects should also be included. Hence, we theorize that integrating game-based learning into the writing environment may be a practical approach that can facilitate student participation, not only helping students learn how to write, but also sustaining their willingness to write. In this study, we investigate the effects of the game-based writing environment on improving students' participation, performance, and interest in writing. An experiment was conducted to compare the effectiveness of 2 approaches to writing in language arts at an elementary school. Two hundred forty-five third grade students participated in the experiment over a period of 1 year. One hundred thirty-nine students were assigned to an experimental group and learned with a game-based writing environment, and 106 students were in the contrast group and learned with an online writing environment. The empirical results show that the game-based writing environment can effectively promote students' writing participation, writing performance, interest in writing, as well as their perceptions of the use of educational self-management games. Some implications of the experimental results are also discussed.

Graduate students usually lack sufficient ability to read academic papers effectively and efficiently. For facilitating their reading comprehension, this study adopts summarization as a main reading strategy under the scaffold of keyword evaluation. Furthermore, this study attempts to transform summarization into a group-based educational game by incorporating keyword auction mechanisms. The research purpose is to investigate how first-year graduate students react to such an educational game when they are required to summarize a section of a real journal paper. The results indicate that they improve the completeness of the summaries without decreasing their conciseness after the game. Besides, the analysis of their prior summaries suggests that graduate students may lack the ability of structure analysis, idea integration and argumentation. Additionally, the results also show that there are positive relationships among the students' evaluation, experts' evaluation, auction behaviors and frequencies in summaries of the keywords, suggesting that students' keyword evaluation may change their decision-making behaviors and the quality of their final summaries.

Methanol is an important intermediate in the utilization of natural gas for synthesizing other feedstock chemicals. Typically, chemical approaches for building C-C bonds from methanol require high temperature and pressure. Biological conversion of methanol to longer carbon chain compounds is feasible; however, the natural biological pathways for methanol utilization involve carbon dioxide loss or ATP expenditure. Here we demonstrated a biocatalytic pathway, termed the methanol condensation cycle (MCC), by combining the nonoxidative glycolysis with the ribulose monophosphate pathway to convert methanol to higher-chain alcohols or other acetyl-CoA derivatives using enzymatic reactions in a carbon-conserved and ATP-independent system. We investigated the robustness of MCC and identified operational regions. We confirmed that the pathway forms a catalytic cycle through (13)C-carbon labeling. With a cell-free system, we demonstrated the conversion of methanol to ethanol or n-butanol. The high carbon efficiency and low operating temperature are attractive for transforming natural gas-derived methanol to longer-chain liquid fuels and other chemical derivatives.

Hedgehog (Hh) pathway inhibitors are clinically effective in treatment of basal cell carcinoma and medulloblastoma, but fail therapeutically or accelerate progression in treatment of endodermally derived colon and pancreatic cancers. In bladder, another organ of endodermal origin, we find that despite its initial presence in the cancer cell of origin Sonic hedgehog (Shh) expression is invariably lost during progression to invasive urothelial carcinoma. Genetic blockade of stromal response to Shh furthermore dramatically accelerates progression and decreases survival time. This cancer-restraining effect of Hh pathway activity is associated with stromal expression of BMP signals, which stimulate urothelial differentiation. Progression is dramatically reduced by pharmacological activation of BMP pathway activity with low-dose FK506, suggesting an approach to management of human bladder cancer.

Abstract Background The modern society primarily relies on petroleum and natural gas for the production of fuels and chemicals. One of the major commodity chemicals 1,2-propanediol (1,2-PDO), which has an annual production of more than 0.5 million tons in the United States, is currently produced by chemical processes from petroleum derived propylene oxide, which is energy intensive and not sustainable. In this study, we sought to achieve photosynthetic production of 1,2-PDO from CO2 using a genetically engineered cyanobacterium Synechococcus elongatus PCC 7942. Compared to the previously reported biological 1,2-PDO production processes which used sugar or glycerol as the substrates, direct chemical production from CO2 in photosynthetic organisms recycles the atmospheric CO2 and will not compete with food crops for arable land. Results In this study, we reported photosynthetic production of 1,2-PDO from CO2 using a genetically engineered cyanobacterium Synechococcus elongatus PCC 7942. Introduction of the genes encoding methylglyoxal synthase (mgsA), glycerol dehydrogenase (gldA), and aldehyde reductase (yqhD) resulted in the production of ~22mg/L 1,2-PDO from CO2. However, a comparable amount of the pathway intermediate acetol was also produced, especially during the stationary phase. The production of 1,2-PDO requires a robust input of reducing equivalents from cellular metabolism. To take advantage of cyanobacteria’s NADPH pool, the synthetic pathway of 1,2-PDO was engineered to be NADPH-dependent by exploiting the NADPH-specific secondary alcohol dehydrogenases which have not been reported for 1,2-PDO production previously. This optimization strategy resulted in the production of ~150mg/L 1,2-PDO and minimized the accumulation of the incomplete reduction product, acetol. Conclusion This work demonstrated that cyanobacteria can be engineered as a catalyst for the photosynthetic conversion of CO2 to 1,2-PDO. This work also characterized two NADPH-dependent sADHs for their catalytic capacity in 1,2-PDO formation, and suggested that they may be useful tools for renewable production of reduced chemicals in photosynthetic organisms.

Abstract Background The model bacterium Clostridium cellulolyticum efficiently degrades crystalline cellulose and hemicellulose, using cellulosomes to degrade lignocellulosic biomass. Although it imports and ferments both pentose and hexose sugars to produce a mixture of ethanol, acetate, lactate, H2 and CO2, the proportion of ethanol is low, which impedes its use in consolidated bioprocessing for biofuels production. Therefore genetic engineering will likely be required to improve the ethanol yield. Plasmid transformation, random mutagenesis and heterologous expression systems have previously been developed for C. cellulolyticum, but targeted mutagenesis has not been reported for this organism, hindering genetic engineering. Results The first targeted gene inactivation system was developed for C. cellulolyticum, based on a mobile group II intron originating from the Lactococcus lactis L1.LtrB intron. This markerless mutagenesis system was used to disrupt both the paralogous L-lactate dehydrogenase (Ccel_2485; ldh) and L-malate dehydrogenase (Ccel_0137; mdh) genes, distinguishing the overlapping substrate specificities of these enzymes. Both mutations were then combined in a single strain, resulting in a substantial shift in fermentation toward ethanol production. This double mutant produced 8.5-times more ethanol than wild-type cells growing on crystalline cellulose. Ethanol constituted 93% of the major fermentation products, corresponding to a molar ratio of ethanol to organic acids of 15, versus 0.18 in wild-type cells. During growth on acid-pretreated switchgrass, the double mutant also produced four times as much ethanol as wild-type cells. Detailed metabolomic analyses identified increased flux through the oxidative branch of the mutant's tricarboxylic acid pathway. Conclusions The efficient intron-based gene inactivation system produced the first non-random, targeted mutations in C. cellulolyticum. As a key component of the genetic toolbox for this bacterium, markerless targeted mutagenesis enables functional genomic research in C. cellulolyticum and rapid genetic engineering to significantly alter the mixture of fermentation products. The initial application of this system successfully engineered a strain with high ethanol productivity from cellobiose, cellulose and switchgrass.

PTEN-controlled PI3K-AKT-mTOR pathway represents one of the most deregulated signaling pathways in human cancers. With many small molecule inhibitors that target PI3K-AKT-mTOR pathway being exploited clinically, sensitive and reliable ways of stratifying patients according to their PTEN functional status and determining treatment outcomes are urgently needed. Heterogeneous loss of PTEN is commonly associated with human cancers and yet PTEN can also be regulated on epigenetic, transcriptional or post-translational levels, which makes the use of simple protein or gene expression-based analyses in determining PTEN status less accurate. In this study, we used network component analysis to identify 20 transcription factors (TFs) whose activities deduced from their target gene expressions were immediately altered upon the re-expression of PTEN in a PTEN-inducible system. Interestingly, PTEN controls the activities (TFA) rather than the expression levels of majority of these TFs and these PTEN-controlled TFAs are substantially altered in prostate cancer mouse models. Importantly, the activities of these TFs can be used to predict PTEN status in human prostate, breast and brain tumor samples with enhanced reliability when compared to straightforward IHC-based or expression-based analysis. Furthermore, our analysis indicates that unique sets of PTEN-controlled TFAs significantly contribute to specific tumor types. Together, our findings reveal that TFAs may be used as "signatures" for predicting PTEN functional status and elucidate the transcriptional architectures underlying human cancers caused by PTEN loss.

Abstract Background Network Component Analysis (NCA) has been used to deduce the activities of transcription factors (TFs) from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies. Results We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number) and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network. Conclusions The advantage of our new algorithm, relative to the original NCA, is that our algorithm can identify the important TF-gene interactions. Identifying the important TF-gene interactions is crucial for understanding the roles of pleiotropic global regulators, such as p53. Also, our algorithm has been developed to overcome NCA's inability to analyze large networks where multiple TFs regulate a single gene. Thus, our algorithm extends the applicability of NCA to the realm of mammalian regulatory network analysis.

The production of isobutanol in microorganisms has recently been achieved by harnessing the highly active 2-keto acid pathways. Since these 2-keto acids are precursors of amino acids, we aimed to construct an isobutanol production platform in Corynebacterium glutamicum, a well-known amino-acid-producing microorganism. Analysis of this host’s sensitivity to isobutanol toxicity revealed that C. glutamicum shows an increased tolerance to isobutanol relative to Escherichia coli. Overexpression of alsS of Bacillus subtilis, ilvC and ilvD of C. glutamicum, kivd of Lactococcus lactis, and a native alcohol dehydrogenase, adhA, led to the production of 2.6 g/L isobutanol and 0.4 g/L 3-methyl-1-butanol in 48 h. In addition, other higher chain alcohols such as 1-propanol, 2-methyl-1-butanol, 1-butanol, and 2-phenylethanol were also detected as byproducts. Using longer-term batch cultures, isobutanol titers reached 4.0 g/L after 96 h with wild-type C. glutamicum as a host. Upon the inactivation of several genes to direct more carbon through the isobutanol pathway, we increased production by ∼25% to 4.9 g/L isobutanol in a ∆pyc∆ldh background. These results show promise in engineering C. glutamicum for higher chain alcohol production using the 2-keto acid pathways.

Deleted in liver cancer 1 (DLC1) is a RhoGTPase activation protein-containing tumor suppressor that associates with various types of cancer. Although DLC2 shares a similar domain structure with that of DLC1, the function of DLC2 is not well characterized. Here, we describe the expression and ablation of DLC2 in mice using a reporter-knockout approach. DLC2 is expressed in several tissues and in endothelial cells (ECs) of blood vessels. Although ECs and blood vessels show no histological abnormalities and mice appear overall healthy, DLC2-mutant mice display enhanced angiogenic responses induced by matrigel and by tumor cells. Silencing of DLC2 in human ECs has reduced cell attachment, increased migration, and tube formation. These changes are rescued by silencing of RhoA, suggesting that the process is RhoA pathway dependent. These results indicate that DLC2 is not required for mouse development and normal vessel formation, but may protect mouse from unwanted angiogenesis induced by, for example, tumor cells.

Interest in producing biofuels from renewable sources has escalated due to energy and environmental concerns. Recently, the production of higher chain alcohols from 2-keto acid pathways has shown significant progress. In this paper, we demonstrate a mutagenesis approach in developing a strain of Escherichia coli for the production of 3-methyl-1-butanol by leveraging selective pressure toward l-leucine biosynthesis and screening for increased alcohol production. Random mutagenesis and selection with 4-aza-d,l-leucine, a structural analogue to l-leucine, resulted in the development of a new strain of E. coli able to produce 4.4 g/L of 3-methyl-1-butanol. Investigation of the host’s sensitivity to 3-methyl-1-butanol directed development of a two-phase fermentation process in which titers reached 9.5 g/L of 3-methyl-1-butanol with a yield of 0.11 g/g glucose after 60 h.

Pentanol isomers such as 2-methyl-1-butanol and 3-methyl-1-butanol are a useful class of chemicals with a potential application as biofuels. They are found as natural by-products of microbial fermentations from amino acid substrates. However, the production titer and yield of the natural processes are too low to be considered for practical applications. Through metabolic engineering, microbial strains for the production of these isomers have been developed, as well as that for 1-pentanol and pentenol. Although the current production levels are still too low for immediate industrial applications, the approach holds significant promise for major breakthroughs in production efficiency.

Biofuels synthesized from renewable resources are of increasing interest because of global energy and environmental problems. We have previously demonstrated production of higher alcohols from Escherichia coli using a 2-keto acid-based pathway. Here, we have compared the effect of various alcohol dehydrogenases (ADH) for the last step of the isobutanol production. E. coli has the yqhD gene which encodes a broad-range ADH. Isobutanol production significantly decreased with the deletion of yqhD, suggesting that the yqhD gene on the genome contributed to isobutanol production. The adh genes of two bacteria and one yeast were also compared in E. coli harboring the isobutanol synthesis pathway. Overexpression of yqhD or adhA in E. coli showed better production than ADH2, a result confirmed by activity measurements with isobutyraldehyde.

The depleting petroleum storage and increasing environmental deterioration are threatening the sustainable development of human societies. As such, biofuels and chemical feedstocks generated from renewable sources are becoming increasingly important. Although previous efforts led to great success in bio-ethanol production, higher alcohols, fatty acid derivatives including biodiesels, alkanes, and alkenes offer additional advantages because of their compatibility with existing infrastructure. In addition, some of these compounds are useful chemical feedstocks. Since native organisms do not naturally produce these compounds in high quantities, metabolic engineering becomes essential in constructing producing organisms. In this article, we briefly review the four major metabolic systems, the coenzyme-A mediated pathways, the keto acid pathways, the fatty acid pathway, and the isoprenoid pathways, that allow production of these fuel-grade chemicals.

Understanding the relationship between genetic variation and gene expression is a central question in genetics. With the availability of data from high-throughput technologies such as ChIP-Chip, expression, and genotyping arrays, we can begin to not only identify associations but to understand how genetic variations perturb the underlying transcription regulatory networks to induce differential gene expression. In this study, we describe a simple model of transcription regulation where the expression of a gene is completely characterized by two properties: the concentrations and promoter affinities of active transcription factors. We devise a method that extends Network Component Analysis (NCA) to determine how genetic variations in the form of single nucleotide polymorphisms (SNPs) perturb these two properties. Applying our method to a segregating population of Saccharomyces cerevisiae, we found statistically significant examples of trans-acting SNPs located in regulatory hotspots that perturb transcription factor concentrations and affinities for target promoters to cause global differential expression and cis-acting genetic variations that perturb the promoter affinities of transcription factors on a single gene to cause local differential expression. Although many genetic variations linked to gene expressions have been identified, it is not clear how they perturb the underlying regulatory networks that govern gene expression. Our work begins to fill this void by showing that many genetic variations affect the concentrations of active transcription factors in a cell and their affinities for target promoters. Understanding the effects of these perturbations can help us to paint a more complete picture of the complex landscape of transcription regulation. The software package implementing the algorithms discussed in this work is available as a MATLAB package upon request.

Isobutanol has emerged as a potential biofuel due to recent metabolic engineering efforts. Here we used gene expression and transcription network connectivity data, genetic knockouts, and network component analysis (NCA) to map the initial isobutanol response network of Escherichia coli under aerobic conditions. NCA revealed profound perturbations to respiration. Further investigation showed ArcA as an important mediator of this response. Quinone/quinol malfunction was postulated to activate ArcA, Fur, and PhoB in this study. In support of this hypothesis, quinone-linked ArcA and Fur target expressions were significantly less perturbed by isobutanol under fermentative growth whereas quinol-linked PhoB target expressions remained activated, and isobutanol impeded growth on glycerol, which requires quinones, more than on glucose. In addition, ethanol, n-butanol, and isobutanol response networks were compared. n-Butanol and isobutanol responses were qualitatively similar, whereas ethanol had notable induction differences of pspABCDE and ndh, whose gene products manage proton motive force. The network described here could aid design and comprehension of alcohol tolerance, whereas the approach provides a general framework to characterize complex phenomena at the systems level.

This paper reports on cell and microparticle manipulation using optically induced dielectrophoresis. Our novel optoelectronic tweezers (OET) device enables optically controlled trapping, transportation, and sorting via dielectrophoretic forces. By integrating a spatial light modulator and using direct imaging, arbitrary dynamic manipulation patterns are obtained. Here, we demonstrate manipulation functions, including particle collectors, single-particle traps, individually addressable single-particle arrays, light-defined particle channels, and size-based particle sorting. OET-induced particle manipulation velocities are analyzed as a function of the applied voltage, optical pattern linewidth, and single-particle trap dimensions.

OBJECTIVE: Janus Kinase (JAK) inhibitors have been extensively evaluated for their potential in the management of various diseases. Despite previous research on this topic, there is a lack of bibliometric analysis that summarizes research trends on JAK inhibitors. This study aims to provide a comprehensive overview of the top 100 most frequently cited studies on JAK inhibitors over the last ten years. MATERIALS AND METHODS: The Web of Science database was used to screen and extract relevant studies on JAK inhibitors. The top 100 studies most cited within the JAK inhibitor-related research were identified and evaluated, and various data such as the year of publication, study focus and keywords, author information, and number of citations were extracted and analyzed for further examination. RESULTS: In the top 100 most cited studies of JAK inhibitors, more than 70% of studies focused on the role of JAK inhibitors in disease treatments, with 42% of these studies focused on using JAK inhibitors as treatment for autoimmune diseases and 19 of them focused on the treatment of neoplasms. Time trend analysis revealed that the keywords “tofacitinib”, “atopic dermatitis”, and “rheumatoid arthritis” were widely mentioned in 2016, while new trends emerged in 2018, with “ruxolitinib” and “baricitinib” being more commonly mentioned. CONCLUSIONS: The top 100 most frequently cited studies on JAK inhibitors focused primarily on the safety and efficacy of these inhibitors in the management of various diseases, particularly inflammatory diseases and neoplasms. The results can serve as a valuable reference for rheumatologists and immunologists interested in the development of JAK inhibitors and expanding future research fields. [ABSTRACT FROM AUTHOR]

Although well-designed scaffolding may assist students to accomplish learning tasks, its insufficient capability to dynamically assess students' abilities and to adaptively support them may result in the problem of overscaffolding. Our previous project has also shown that students using scaffolds to solve mathematical word problems for a long time may regard problem solving as a repetitive job and eventually became bored. To avoid the problem, this study redesigns the activity model of scaffolding by adopting a reverse rationale and incorporating the concept of helps seeking. Furthermore, this study proposes the activity model of scaffold seeking and designs a system that empowers students to seek scaffolds for solving mathematical word problems. A case study shows that the model may encourage students to solve problems by themselves rather than abusing scaffolds. Besides, the result also suggests that students are potential to express their thinking without the limitation of scaffolds.

Optimized Policies for Adaptive Control (OPAC) is a real-time demand-responsive traffic signal timing optimization algorithm for individual intersections. It was developed at University of Lowell under the sponsorship of U.S. Department of Transportation in the early 80s. OPAC distinguishes itself from traditional cycle-split signal control strategies by dropping the concept of cycle. In OPAC, the signal control problem consists of a sequence of switching decisions made at fixed time intervals. At each decision point the question is whether to extend or terminate current phase. Dynamic programming techniques are used to calculate optimal solutions. OPAC utilizes on-line data obtained from upstream detectors as well as historical data in the optimization. The objective is to minimize performance measures, such as vehicle delays and stops. Each phase is constrained only by the minimum and maximum phase lengths. Consequently, the duration of a phase is never prespecified. It depends solely on the prevailing traffic flow conditions. The dynamic optimization process is carried out continuously to ensure that the signal control is always up-to-date.

This working paper is the second part of a series comparing dynamic traffic flow models. It documents the results of comparison based on the framework defined in Part 1. The traffic models selected for comparison are DINOSAUR, DYNASMART, INTEGRATION, and METS. The areas of comparison comprise four categories: functionality, traffic dynamics, route choice mechanism, and network performance.The first category was compared with a checklist of functions. A total of thirteen test scenarios were constructed to compare models for the last three categories.

This report defines a framework for comparing dynamic traffic models. It emphasizes four dimensions: functionality, traffic dynamics, route choice dy namics, and overall network performance. The first dimension compares the models through a check-list of model functions. Regarding the last three comparison dimensions, a total of five networks and twelve scenarios are defined. These test scenarios are designed to accentuate model properties and differences. Also included in the report are a list of performance measures for comparison purpose, and a discussion of the interpretation of results.