Topoisomerase 1 activity during mitotic transcription favors the transition from mitosis to G1.

As cells enter mitosis, chromatin compacts to facilitate chromosome segregation yet remains transcribed. Transcription supercoils DNA to levels that can impede further progression of RNA polymerase II (RNAPII) unless it is removed by DNA topoisomerase 1 (TOP1). Using ChIP-seq on mitotic cells, we found that TOP1 is required for RNAPII translocation along genes. The stimulation of TOP1 activity by RNAPII during elongation allowed RNAPII clearance from genes in prometaphase and enabled chromosomal segregation. Disruption of the TOP1-RNAPII interaction impaired RNAPII spiking at promoters and triggered defects in the post-mitotic transcription program. This program includes factors necessary for cell growth, and cells with impaired TOP1-RNAPII interaction are more sensitive to inhibitors of mTOR signaling. We conclude that TOP1 is necessary for assisting transcription during mitosis with consequences for growth and gene expression long after mitosis is completed. In this sense, TOP1 ensures that cellular memory is preserved in subsequent generations.

Confirming High-Valent Iron as Highly Active Species of Water Oxidation on the Fe, V-Coupled Bimetallic Electrocatalyst: In Situ Analysis of X-ray Absorption and Mössbauer Spectroscopy.

Iron (Fe)-based bimetallic oxides/hydroxides have been widely investigated for promising alkaline electrochemical oxygen evolution reactions (OERs), but it still remains argumentative whether Fe3+ or Fe4+ intermediates are highly active for efficient OER. Here, we rationally designed and prepared one Fe, V-based bimetallic composite nanosheet by employing the OER-inert V element as a promoter to completely avoid the argument of real active metals and using our recently developed one-dimensional conductive nickel phosphide (NP) as a support. The as-obtained hierarchical nanocomposite (denoted as FeVOx/NP) was evaluated as a model catalyst to gain insight into the iron-based species as highly active OER sites by performing in situ X-ray absorption spectroscopy and 57Fe Mössbauer spectroscopy measurements. It was found that the high-valent Fe4+ species can only be detected during the OER process of the FeVOx/NP nanocomposite instead of the iron counterpart itself. Together with the fact that the OER activities of both the vanadium and iron counterparts are by far worse than that of the FeVOx/NP composite, we can confirm that the high-valent Fe4+ formed are the highly active species for efficient OER. As demonstrated by density functional theory simulations, the composite of Fe and V metals is proposed to cause a decreased Gibbs free energy as well as theoretical overpotential of water oxidation with respect to its counterparts, as is responsible for its excellent OER performance with extremely low OER overpotential (290 mV at 500 mA cm-2) and extraordinary stability (1000 h at 100 mA cm-2).

Absence of GSTT1 and polymorphisms in GSTP1 and TP53 are associated with the incidence of acne vulgaris.

BACKGROUNDS: Acne vulgaris is a chronic inflammatory skin disease of the pilosebaceous unit affecting most teenagers and numerous adults throughout the world. The present study was designed to assess the association of the presence or absence of GSTM1, GSTT1, and single nucleotide polymorphisms rs1695 in GSTP1 and rs1042522 in TP53 gene with acne vulgaris. METHODS: The cross-sectional case-control study was conducted at the Institute of Zoology from May 2020 to March 2021 and included acne vulgaris patients (N = 100) and controls (N = 100) enrolled in Dera Ghazi Khan district, Pakistan. Multiplex and tetra-primer amplification refractory mutation system-polymerase chain reactions were applied to investigate the genotype in analyzed genes. The association of rs1695 and rs1042522 with acne vulgaris was studied either individually or in various combinations with GATM1 and T1. RESULTS: A significant association of absence of GSTT1 and mutant genotype at rs1695 (GG) and at rs1042522 (CC) in GSTP1 and TP53, respectively, was found to be associated with acne vulgaris in enrolled subjects. Subjects aged 10-25 years and smokers were more susceptible to acne vulgaris. CONCLUSION: Our results suggest that genotypes of glutathione S-transferases (GSTs) and TP53 are involved in protection against oxidative stress and may influence disease progression in acne vulgaris.

Importin α2 association with chromatin: Direct DNA binding via a novel DNA-binding domain.

The nuclear transport of proteins is important for facilitating appropriate nuclear functions. The importin α family proteins play key roles in nuclear transport as transport receptors for copious nuclear proteins. Additionally, these proteins possess other functions, including chromatin association and gene regulation. However, these nontransport functions of importin α are not yet fully understood, especially their molecular-level mechanisms and consequences for functioning with chromatin. Here, we report the novel molecular characteristics of importin α binding to diverse DNA sequences in chromatin. We newly identified and characterized a DNA-binding domain-the Nucleic Acid Associating Trolley pole domain (NAAT domain)-in the N-terminal region of importin α within the conventional importin ß binding (IBB) domain that is necessary for nuclear transport of cargo proteins. Furthermore, we found that the DNA binding of importin α synergistically coupled the recruitment of its cargo protein to DNA. This is the first study to delineate the interaction between importin α and chromatin DNA via the NAAT domain, indicating the bifunctionality of the importin α N-terminal region for nuclear transport and chromatin association.

Developing Smartness in Emerging Environments and Applications with a Focus on the Internet of Things.

The smartness that underpins smart cities and societies is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner [...].

Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge.

Several factors are motivating the development of preventive, personalized, connected, virtual, and ubiquitous healthcare services. These factors include declining public health, increase in chronic diseases, an ageing population, rising healthcare costs, the need to bring intelligence near the user for privacy, security, performance, and costs reasons, as well as COVID-19. Motivated by these drivers, this paper proposes, implements, and evaluates a reference architecture called Imtidad that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over cloud, fog, and edge using a service catalog case study containing 22 AI skin disease diagnosis services. These services belong to four service classes that are distinguished based on software platforms (containerized gRPC, gRPC, Android, and Android Nearby) and are executed on a range of hardware platforms (Google Cloud, HP Pavilion Laptop, NVIDIA Jetson nano, Raspberry Pi Model B, Samsung Galaxy S9, and Samsung Galaxy Note 4) and four network types (Fiber, Cellular, Wi-Fi, and Bluetooth). The AI models for the diagnosis include two standard Deep Neural Networks and two Tiny AI deep models to enable their execution at the edge, trained and tested using 10,015 real-life dermatoscopic images. The services are evaluated using several benchmarks including model service value, response time, energy consumption, and network transfer time. A DL service on a local smartphone provides the best service in terms of both energy and speed, followed by a Raspberry Pi edge device and a laptop in fog. The services are designed to enable different use cases, such as patient diagnosis at home or sending diagnosis requests to travelling medical professionals through a fog device or cloud. This is the pioneering work that provides a reference architecture and such a detailed implementation and treatment of DAIaaS services, and is also expected to have an extensive impact on developing smart distributed service infrastructures for healthcare and other sectors.

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired.

Over a billion people around the world are disabled, among whom 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, and poor environments and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required in order to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach using a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on the smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image-processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, having constructed their prototype implementations and tested them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than USD 80. Essentially, we provide designs of an inexpensive, miniature green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach enables faster inference and decision-making using relatively low energy with smaller data sizes, as well as faster communications for edge, fog, and cloud computing.

Water-Stable Cobalt-Based MOF for Water Oxidation in Neutral Aqueous Solution: A Case of Mimicking the Photosystem II.

Inspired by the highly efficient water oxidation of Mn4CaO5 in natural photosynthesis, development of novel artificial water oxidation catalysts (WOCs) with structure and function mimicked has inspired extensive interests. A novel 3D cobalt-based MOF (GXY-L8-Co) was synthesized for promising artificial water oxidation by employing the Co4O4 quasi-cubane motifs with a similar structure as the Mn4CaO5 as the core. The GXY-L8-Co not only shows good chemical stability in common organic solvents or water for up to 10 days but also exhibits oxygen evolution performance. It has been demonstrated that the uniform distribution of Co4O4 catalytic active sites confined in the MOF framework should be responsible for the good robustness and catalytic performance.

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning.

Digital societies could be characterized by their increasing desire to express themselves and interact with others. This is being realized through digital platforms such as social media that have increasingly become convenient and inexpensive sensors compared to physical sensors in many sectors of smart societies. One such major sector is road transportation, which is the backbone of modern economies and costs globally 1.25 million deaths and 50 million human injuries annually. The cutting-edge on big data-enabled social media analytics for transportation-related studies is limited. This paper brings a range of technologies together to detect road traffic-related events using big data and distributed machine learning. The most specific contribution of this research is an automatic labelling method for machine learning-based traffic-related event detection from Twitter data in the Arabic language. The proposed method has been implemented in a software tool called Iktishaf+ (an Arabic word meaning discovery) that is able to detect traffic events automatically from tweets in the Arabic language using distributed machine learning over Apache Spark. The tool is built using nine components and a range of technologies including Apache Spark, Parquet, and MongoDB. Iktishaf+ uses a light stemmer for the Arabic language developed by us. We also use in this work a location extractor developed by us that allows us to extract and visualize spatio-temporal information about the detected events. The specific data used in this work comprises 33.5 million tweets collected from Saudi Arabia using the Twitter API. Using support vector machines, naïve Bayes, and logistic regression-based classifiers, we are able to detect and validate several real events in Saudi Arabia without prior knowledge, including a fire in Jeddah, rains in Makkah, and an accident in Riyadh. The findings show the effectiveness of Twitter media in detecting important events with no prior knowledge about them.

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments.

Artificial intelligence (AI) has taken us by storm, helping us to make decisions in everything we do, even in finding our "true love" and the "significant other". While 5G promises us high-speed mobile internet, 6G pledges to support ubiquitous AI services through next-generation softwarization, heterogeneity, and configurability of networks. The work on 6G is in its infancy and requires the community to conceptualize and develop its design, implementation, deployment, and use cases. Towards this end, this paper proposes a framework for Distributed AI as a Service (DAIaaS) provisioning for Internet of Everything (IoE) and 6G environments. The AI service is "distributed" because the actual training and inference computations are divided into smaller, concurrent, computations suited to the level and capacity of resources available with cloud, fog, and edge layers. Multiple DAIaaS provisioning configurations for distributed training and inference are proposed to investigate the design choices and performance bottlenecks of DAIaaS. Specifically, we have developed three case studies (e.g., smart airport) with eight scenarios (e.g., federated learning) comprising nine applications and AI delivery models (smart surveillance, etc.) and 50 distinct sensor and software modules (e.g., object tracker). The evaluation of the case studies and the DAIaaS framework is reported in terms of end-to-end delay, network usage, energy consumption, and financial savings with recommendations to achieve higher performance. DAIaaS will facilitate standardization of distributed AI provisioning, allow developers to focus on the domain-specific details without worrying about distributed training and inference, and help systemize the mass-production of technologies for smarter environments.

Can Building "Artificially Intelligent Cities" Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar's Perspective.

In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has the potential to dramatically change our cities and societies. Despite its growing importance, the urban and social implications of AI are still an understudied area. In order to contribute to the ongoing efforts to address this research gap, this paper introduces the notion of an artificially intelligent city as the potential successor of the popular smart city brand-where the smartness of a city has come to be strongly associated with the use of viable technological solutions, including AI. The study explores whether building artificially intelligent cities can safeguard humanity from natural disasters, pandemics, and other catastrophes. All of the statements in this viewpoint are based on a thorough review of the current status of AI literature, research, developments, trends, and applications. This paper generates insights and identifies prospective research questions by charting the evolution of AI and the potential impacts of the systematic adoption of AI in cities and societies. The generated insights inform urban policymakers, managers, and planners on how to ensure the correct uptake of AI in our cities, and the identified critical questions offer scholars directions for prospective research and development.

Novel Maleic Acid, Crosslinked, Nanofibrous Chitosan/Poly (Vinylpyrrolidone) Membranes for Reverse Osmosis Desalination.

Fresh and clean water is consistently depleting and becoming a serious problem with rapid increases in population, so seawater desalination technology has captured global attention. For an efficient desalination process, this work proposes a novel, nanofibrous, thin-film composite membrane (NF-TFC) based on the deposition of the nanofibrous active layer of a blend of chitosan (CS) and poly (vinylpyrrolidone) (PVP) crosslinked with maleic acid on a 3-triethoxysilylpropylamine functionalized cellulose acetate substrate. FTIR analysis demonstrated the development of chemical and physical interactions and confirmed the incorporation of functional groups present in the NF-TFC. Scanning electron microscopy (SEM) micrographs depict the fibrous structure of the active layers. The reverse osmosis (RO) desalination characteristics of NF-TFC membranes are elevated by increasing the concentration of the crosslinker in a CS/PVP blend. Cellulose acetate (CA)-S4 attained an optimal salt rejection of 98.3% and permeation flux of 42.9 L/m2h, suggesting that the NF-TFC membranes could be favorable for seawater desalination.

Surface, Subsurface, and Bulk Oxygen Vacancies Quantified by Decoupling and Deconvolution of the Defect Structure of Redox-Active Nanoceria.

Oxygen vacancy concentrations are critical to the redox/photocatalytic performance of nanoceria, but their direct analysis is problematic under controlled atmospheres but essentially impossible under aqueous conditions. The present work provides three novel approaches to analyze these data from XPS data for the three main morphologies of nanoceria synthesized under aqueous conditions and tested using in vacuo analytical conditions. First, the total oxygen vacancy concentrations are decoupled quantitatively into surface-filled, subsurface-unfilled, and bulk values. Second, the relative surface areas are calculated for all exposed crystallographic planes. Third, XPS and redox performance data are deconvoluted according to the relative surface areas of these planes. Correlations based on two independent empirical results from volumetric surface XPS, combined with sequential deep XPS and independent EELS data, confirm that these approaches provide quantitative determinations of the different oxygen vacancy concentrations. Critically, the redox/photocatalytic performance depends not on the total oxygen vacancy concentration but on the concentration of the active sites on each plane in the form of subsurface-unfilled oxygen vacancies. This is verified by the pH-dependent performance, which can be increased significantly by exposing these vacancies to the surroundings. These approaches have significance to the design and engineering of semiconducting materials exposed to the environment.

Maternal BMI mediates the impact of crop-related agricultural work during pregnancy on infant length in rural Pakistan: a mediation analysis of cross-sectional data.

BACKGROUND: Stunted growth in early infancy is a public health problem in low-and-middle income countries. Evidence suggests heavy agricultural work during pregnancy is inversely associated with maternal body mass index (BMI) and infant birth weight in low- and middle-income countries; but pathways linking agricultural work to length-for-age Z-scores (LAZ) in early infancy have not been examined. This study aimed to investigate the relationship between agricultural work during pregnancy, post-natal maternal BMI and LAZ among young infants in rural Pakistan; and explored whether maternal BMI mediated the relationship between agricultural work and infant LAZ. METHODS: A cross-sectional survey was conducted from December 2015 to January 2016 in rural Sindh, Pakistan. Mother-infant dyads were recruited via systematic random cluster sampling at 2-12 weeks' post-partum (n = 1161). Anthropometric measurements (maternal and infant height/length and weight) and questionnaire data were collected. Multivariable linear regression and structural-equation based mediation analyses were used to examine associations of agricultural work during pregnancy with maternal BMI and infant LAZ. RESULTS: During pregnancy, women reported engaging in livestock-related work (57.0%), crop-related work (42.7%), and cotton harvesting (28.4%). All three forms of agricultural work were negatively associated with maternal BMI (ß = - 0.67 [- 1.06; - 0.28], ß = - 0.97 [- 1.51; - 0.48]; and ß = - 0.87 [- 1.33; - 0.45], respectively). Maternal engagement in cotton harvesting alone was negatively associated with infant LAZ after controlling for confounding factors. The total negative effect of cotton harvesting on infant LAZ was - 0.35 [- 0.53; - 0.16]. The indirect effect of maternal BMI on infant LAZ was - 0.06 [- 0.08; - 0.03], revealing that 16% (- 0.06/- 0.35) of the relationship between cotton harvesting and infant LAZ, after adjustment, was mediated via maternal BMI. CONCLUSION: These results underscore a need to reduce labour-intensive agricultural workload demands during pregnancy, especially in cotton harvesting, to reduce risks of negative maternal energy balance and poor growth outcomes in early infancy.

Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs.

Road transportation is the backbone of modern economies, albeit it annually costs 1.25 million deaths and trillions of dollars to the global economy, and damages public health and the environment. Deep learning is among the leading-edge methods used for transportation-related predictions, however, the existing works are in their infancy, and fall short in multiple respects, including the use of datasets with limited sizes and scopes, and insufficient depth of the deep learning studies. This paper provides a novel and comprehensive approach toward large-scale, faster, and real-time traffic prediction by bringing four complementary cutting-edge technologies together: big data, deep learning, in-memory computing, and Graphics Processing Units (GPUs). We trained deep networks using over 11 years of data provided by the California Department of Transportation (Caltrans), the largest dataset that has been used in deep learning studies. Several combinations of the input attributes of the data along with various network configurations of the deep learning models were investigated for training and prediction purposes. The use of the pre-trained model for real-time prediction was explored. The paper contributes novel deep learning models, algorithms, implementation, analytics methodology, and software tool for smart cities, big data, high performance computing, and their convergence.

Factors associated with catch-up growth in early infancy in rural Pakistan: A longitudinal analysis of the women's work and nutrition study.

The adverse health impacts of early infant stunting can be partially ameliorated by early catch-up growth. Few studies have examined predictors of and barriers to catch-up growth to identify intervention points for improving linear growth during infancy. This study aimed to estimate the prevalence of, and factors associated with, catch-up growth among infants in Pakistan. A longitudinal study of mother-infant dyads (n = 1,161) was conducted in rural Sindh province, with enrolment between December 2015 and February 2016 (infants aged 0.5-3 months), and follow-up (n = 1035) between November 2016 and January 2017 (infants aged 9-15 months). The outcome was catch-up growth (change in conditional length-for-age z-scores >0.67 between baseline and endline). Associated factors were examined using multivariable logistic regression analyses. The prevalence of stunting was 45.3% at baseline and 60.7% at follow-up. 22.8% of infants exhibited catch-up growth over this period. Factors positively associated with catch-up growth included maternal height (odds ratio (OR) = 1.08 [1.05-1.11]), household wealth (OR = 3.61 [1.90-6.84]), maternal (OR = 2.43 [1.30-4.56]) or paternal (OR = 1.46 [1.05-2.03]) education, and households with two or more adult females (OR = 1.91 [1.26-2.88]). Factors negatively associated with catch-up growth were two (OR = 0.64 [0.45-0.89]) or three or more (OR = 0.44 [0.29-0.66]) preschool children in the household and the infant being currently breastfed (OR = 0.59 [0.41-0.88]). Catch-up growth was exhibited among approximately a quarter of infants despite living in challenging environments associated with extremely high rates of early infant stunting. Several modifiable factors were identified that might represent suitable programme intervention points to off-set early infant stunting in rural Pakistan.

Real Time 3D Representation and Tracking of Guidewire for Image Guided Cardiovascular Interventions.

Visual tracking and 3D representation of guidewire in fluoroscopic image sequence for beating heart image guided interventions is very challenging task. The degraded image quality due to low dose fluoroscopy further complicates the problem. In this paper a robust guidewire tracking is proposed for mean shift algorithm using integrated colour, texture and depth features. The target colour, texture and depth features are encoded into gray level intensity histogram, filtered local binary pattern histogram and filtered local depth pattern histograms respectively. For depth features a 3D image acquisition system for C-Arm, X-Ray imaging system is simulated for real time three dimensional shape recovery of guidewire and associated vessels for vertical beating heart motion using shape from focus technique. The proposed technique provides 3D visualization of guide wire and vessels to the physician as well as real time robust guidewire tip tracking. Experimental results of guidewire tip tracking and 3D shape recovery on image sequence acquired through beating heart simulated phantom show the significance of the proposed technique.

Atp2c2 Is Transcribed From a Unique Transcriptional Start Site in Mouse Pancreatic Acinar Cells.

Proper regulation of cytosolic Ca(2+) is critical for pancreatic acinar cell function. Disruptions in normal Ca(2+) concentrations affect numerous cellular functions and are associated with pancreatitis. Membrane pumps and channels regulate cytosolic Ca(2+) homeostasis by promoting rapid Ca(2+) movement. Determining how expression of Ca(2+) modulators is regulated and the cellular alterations that occur upon changes in expression can provide insight into initiating events of pancreatitis. The goal of this study was to delineate the gene structure and regulation of a novel pancreas-specific isoform for Secretory Pathway Ca(2+) ATPase 2 (termed SPCA2C), which is encoded from the Atp2c2 gene. Using Next Generation Sequencing of RNA (RNA-seq), chromatin immunoprecipitation for epigenetic modifications and promoter-reporter assays, a novel transcriptional start site was identified that promotes expression of a transcript containing the last four exons of the Atp2c2 gene (Atp2c2c). This region was enriched for epigenetic marks and pancreatic transcription factors that promote gene activation. Promoter activity for regions upstream of the ATG codon in Atp2c2's 24th exon was observed in vitro but not in in vivo. Translation from this ATG encodes a protein aligned with the carboxy terminal of SPCA2. Functional analysis in HEK 293A cells indicates a unique role for SPCA2C in increasing cytosolic Ca(2+) . RNA analysis indicates that the decreased Atp2c2c expression observed early in experimental pancreatitis reflects a global molecular response of acinar cells to reduce cytosolic Ca(2+) levels. Combined, these results suggest SPCA2C affects Ca(2+) homeostasis in pancreatic acinar cells in a unique fashion relative to other Ca(2+) ATPases. J. Cell. Physiol. 231: 2768-2778, 2016. © 2016 Wiley Periodicals, Inc.

Targeting the bHLH transcriptional networks by mutated E proteins in experimental glioma.

Glioblastomas (GB) are aggressive primary brain tumors. Helix-loop-helix (HLH, ID proteins) and basic HLH (bHLH, e.g., Olig2) proteins are transcription factors that regulate stem cell proliferation and differentiation throughout development and into adulthood. Their convergence on many oncogenic signaling pathways combined with the observation that their overexpression in GB correlates with poor clinical outcome identifies these transcription factors as promising therapeutic targets. Important dimerization partners of HLH/bHLH proteins are E proteins that are necessary for nuclear translocation and DNA binding. Here, we overexpressed a wild type or a dominant negative form of E47 (dnE47) that lacks its nuclear localization signal thus preventing nuclear translocation of bHLH proteins in long-term glioma cell lines and in glioma-initiating cell lines and analyzed the effects in vitro and in vivo. While overexpression of E47 was sufficient to induce apoptosis in absence of bHLH proteins, dnE47 was necessary to prevent nuclear translocation of Olig2 and to achieve similar proapoptotic responses. Transcriptional analyses revealed downregulation of the antiapoptotic gene BCL2L1 and the proproliferative gene CDC25A as underlying mechanisms. Overexpression of dnE47 in glioma-initiating cell lines with high HLH and bHLH protein levels reduced sphere formation capacities and expression levels of Nestin, BCL2L1, and CDC25A. Finally, the in vivo induction of dnE47 expression in established xenografts prolonged survival. In conclusion, our data introduce a novel approach to jointly neutralize HLH and bHLH transcriptional networks activities, and identify these transcription factors as potential targets in glioma.

Monitoring of Resistance to New Chemistry Insecticides in Spodoptera litura (Lepidoptera: Noctuidae) in Pakistan.

The armyworm Spodoptera litura (F.) is a very serious pest of many crops, vegetables, and ornamentals. Due to persistent application of insecticides, this pest has developed resistance to conventional as well as new chemistries. The present studies investigated the level of resistance to new chemistry insecticides, having novel modes of action, in the Pakistani field populations of S. litura during 1997-2010 by using a leaf-dip bioassay. Generally, a low to moderate resistance was recorded to indoxacarb, abamectin, fipronil, and methoxyfenozide; along with a very low resistance to spinosad. Resistance to emamectin benzoate, chlorfenapyr, and lufenuron was none or very low, in spite of their intensive use against S. litura. The insecticides, showing no or very low resistance, can be used in rotation in conjunction with other integrated pest management tactics to manage insecticide resistance in S. litura.