Search

With the launch of a mega drilling project in the Middle East, the drilling data during the execution stage was collected in two formats; Low-Frequency Data and High-Frequency Data. This paper explains the effective utilization of data in the performance enhancement scheme. The paper also demonstrates the combination of Low-frequency and High-frequency data can reveal the many secrets of the drilling operations and can open the many sides of drilling operations for improvements. Low-Frequency data was entered manually at the rig-site using an improved coding system to identify the activities start and end times. High-Frequency data was collected through real-time transmission from the different data streaming services at the rig-site. Both data forms were collected simultaneously using stringent rules and close follow-ups to make sure that data collection was free of any reporting mistakes and gaps. Later, the collected data was extracted for different types of analyses and interpretations. Low-frequency data was studied in a novel way to get the best analytical and critical outcome to make sure that the right areas for improvements were identified and actions were implemented for enhanced performance. Improved operations coding system helped the team to categorize the operations and failures in an effective way to set new standards in data analysis. More than 100 different types of analyses using the best data analysis technique, such as trailing average, normalization, trends, etc., were conducted based on the information collected during the execution phase, and many new KPIs were established with challenging milestones to be achieved in the prescribed period. High-Frequency data was split into different sets of KPIs to identify the multiple Invisible Lost Time (ILT) areas to boost the operational efficiency. Various performance enhancement schemes were developed based on High-frequency data. As a result, these schemes were proven to enhance the performance of the mega drilling project. This paper discusses the novel methods of drilling data analysis based on low and high-frequency data and shows the effectiveness of the data presented in a standardized format over a period. It deliberates how the teams were challenged to enhance the performance. Such detailed data analysis will bring valuable information for the industry to utilize the conventional database in modernized ways to get the best outcomes from the data analysis results.

IntroductionCortical thickness (CT) and surface area (SA) are established biomarkers of brain pathology in posttraumatic stress disorder (PTSD). Structural covariance networks (SCN) constructed from CT and SA may represent developmental associations, or unique interactions between brain regions, possibly influenced by a common causal antecedent. The ENIGMA-PGC PTSD Working Group aggregated PTSD and control subjects’ data from 29 cohorts in five countries (n=3439).MethodsUsing Destrieux Atlas, we built SCNs and compared centrality measures between PTSD subjects and controls. Centrality is a graph theory measure derived using SCN.ResultsNotable nodes with higher CT-based centrality in PTSD compared to controls were left fusiform gyrus, left superior temporal gyrus, and right inferior temporal gyrus. We found sex-based centrality differences in bilateral frontal lobe regions, left anterior cingulate, left superior occipital cortex and right ventromedial prefrontal cortex (vmPFC). Comorbid PTSD and MDD showed higher CT-based centrality in the right anterior cingulate gyrus, right parahippocampal gyrus and lower SA-based centrality in left insular gyrus.ConclusionUnlike previous studies with smaller sample sizes (≤318), our study found differences in centrality measures using a sample size of 3439 subjects. This is the first cross-sectional study to examine SCN interactions with age, sex, and comorbid MDD. Although limited to group level inferences, centrality measures offer insights into a node’s relationship to the entire functional connectome unlike approaches like seed-based connectivity or independent component analysis. Nodes having higher centrality have greater structural or functional connections, lending them invaluable for translational treatments like neuromodulation.

Graph convolution network (GCN) is an important method recently developed for few-shot learning. The adjacency matrix in GCN models is constructed based on graph node features to represent the graph node relationships, according to which, the graph network achieves message-passing inference. Therefore, the representation ability of graph node features is an important factor affecting the learning performance of GCN. This paper proposes an improved GCN model with node feature optimization using cross attention, named GCN-NFO. Leveraging on cross attention mechanism to associate the image features of support set and query set, the proposed model extracts more representative and discriminative salient region features as initialization features of graph nodes through information aggregation. Since graph network can represent the relationship between samples, the optimized graph node features transmit information through the graph network, thus implicitly enhances the similarity of intra-class samples and the dissimilarity of inter-class samples, thus enhancing the learning capability of GCN. Intensive experimental results on image classification task using different image datasets prove that GCN-NFO is an effective few-shot learning algorithm which significantly improves the classification accuracy, compared with other existing models.

The information on the reproductive biology of Schizothorax niger (snowtrout) will help to know the inception and period of maturity stages, age at first maturity, sex ratio, spawning, and fecundity of this fish. These parameters are fundamental and will help in research, stock assessment, and management of this fish species. Size at first maturity of Schizothorax niger is 14.82 cm. The sex ratio of Schizothorax niger male:female is 1:2.44. During spawning season the gonadosomatic index (GSI) which is recorded is highest in the month of February for females (15.75) and particularly for males it is (12.5) signifying Schizothorax niger matures during the month of February and spawns during March–May and peak season in the month of March ensuring in an unexpected decline in GSI values of the fish. The above data claims that the fish is an annual breeder. Absolute fecundity is 11709 ± 8016 and at some time relative fecundity is 4645 ± 1804. The main plan of this chapter is to provide detailed information about the reproductive and breeding biology of snowtrout, which will be useful for devising the strategies for management of stock of this particular fish.

Reproduction process is important for fish population productivity and in its resilience to fishing and environmental changes. Reproductive potential is the ability to generate feasible amount of quality eggs in relation to the energy available and parental life expectancy. The important parameters for estimating the reproductive potential of the fish are maturity, fecundity, sex ratio, and fish condition which directly affect fish population productivity. Euthynnus affinis in the Veraval region of Gujarat is caught by using drift gillnets throughout the year with peak during December to March. The overall sex ratio is 0.82. The spawning occurs round the year with peak during December to March. The size at first maturity ranges from 55 to 60 cm, whereas absolute fecundity ranged from 171,550.4 to 827,734.35/kg body weight. The size of ova varied from 0.34 to 0.61 mm. Gonadosomatic index is highest during the month of December. Opportunistic predatory nature is in adults, and the most frequently encountered food was fishes, shrimps, Decapterus, squids, and digested fish.

Three new organotin(IV) carboxylate compounds were synthesized and structurally characterized by elemental analysis and FT-IR and multinuclear NMR (1H, 13C, 119Sn) spectroscopy. Single X-ray crystallography reveals that compound C2 has a monoclinic crystal system with space group P21/c having distorted bipyramidal geometry defined by C3SnO2. The synthesized compounds were screened for drug-DNA interactions via UV-Vis spectroscopy and cyclic voltammetry showing good activity with high binding constants. Theoretical investigations also support the reactivity of the compounds as depicted from natural bond orbital (NBO) analysis using Gaussian 09. Synthesized compounds were initially evaluated on two cancer (HeLa and MCF-7) cell lines and cytotoxicity to normal cells was evaluated using a non-cancerous (BHK-21) cell line. All the compounds were found to be active, with IC50 values less than that of the standard drug i.e. cisplatin. The cytotoxic effect of the most potent compound C2 was confirmed by LDH cytotoxicity assay and fluorescence imaging after PI staining. Apoptotic features in compound C2 treated cancer cells were visualized after DAPI staining while regulation of apoptosis was observed by reactive oxygen species generation, binding of C2 with DNA, a change in mitochondrial membrane potential and expression of activated caspase-9 and caspase-3 in cancer cells. Results are indicative of activation of the intrinsic pathway of apoptosis in C2 treated cancer cells.

Deep learning has attracted increasing attention in brain imaging, but many neuroimaging data samples are small and fail to meet the training data requirements to optimize performance. In this study, we propose a deep transfer learning network based on Optimal Mass Transport (OMTNet) for 3D brain image classification using MRI scans from the UK Biobank. The major contributions of the OMTNet method include: a way to map 3D surface-based vertex-wise brain shape metrics, including cortical thickness, surface area, curvature, sulcal depth, and subcortical radial distance and surface Jacobian determinant metrics, onto 2D planar images for each MRI scan based on area-preserving mapping. Such that some popular 2D convolution neural networks pretrained on the ImageNet database, such as ResNet152 and DenseNet201, can be used for transfer learning of brain shape metrics. We used a score-fusion strategy to fuse all shape metrics and generate an ensemble classification. We tested the approach in a classification task conducted on 26k participants from the UK Biobank, using body mass index (BMI) thresholds as classification labels (normal vs. obese BMI). Ensemble classification accuracies of 72.8 ± 1.2% and 73.9 ± 2.3% were obtained for ResNet152 and DenseNet201 networks that used transfer learning, with 5.4–12.3% and 6.1–13.0% improvements relative to classifications based on single shape metrics, respectively. Transfer learning always outperformed direct learning and conventional linear support vector machines with 3.4–8.7% and 4.9–6.0% improvements in ensemble classification accuracies, respectively. Our proposed OMTNet method may offer a powerful transfer learning framework that can be extended to other vertex-wise brain structural/functional imaging measures.

Substitution of hazardous and often harmful organic solvents with "green" and "sustainable" alternative reaction media is always desirous. Ionic liquids (IL) have emerged as valuable and versatile liquids that can replace most organic solvents in a variety of syntheses. However, recently new types of low melting mixtures termed as Deep Eutectic Solvents (DES) have been utilized in organic syntheses. DES are non-volatile in nature, have sufficient thermal stability, and also have the ability to be recycled and reused. Hence DES have been used as alternative reaction media to perform different organic reactions. The availability of green, inexpensive and easy to handle alternative solvents for organic synthesis is still scarce, hence our interest in DES mediated syntheses. Herein we have investigated Biginelli reaction in different DES for the synthesis of 3,4-dihydropyrimidin-2(1H)-ones. Monoamine oxidases and cholinesterases are important drug targets for the treatment of various neurological disorders such as Alzheimer's disease, Parkinson's disease, depression and anxiety. The compounds synthesized herein were evaluated for their inhibitory potential against these enzymes. Some of the compounds were found to be highly potent and selective inhibitors. Compounds 1 h and 1c were the most active monoamine oxidase A (MAO A) (IC50 = 0.31 ± 0.11 µM) and monoamine oxidase B (MAO B) (IC50 = 0.34 ± 0.04 µM) inhibitors respectively. All compounds were selective AChE inhibitors and did not inhibit BChE (

Debates in international forums and in mainstream media on the role, responsibility, liability, and response of ecclesiastical authorities of the Roman Catholic Church (RCC) toward clerical child sexual abuse (cCSA) fail to take into account the historical roots and awareness of the problem. Reports also fail to mention the historic organizational laws RCC developed over centuries. In contrast, RCC documents evidence that the Catholic Church not only carried century's old history of cCSA, but also repeatedly condemned cCSA by successive papal authorities, organizational laws, and institutional management mechanisms. During the first millennium, however, church laws remained confined to the bookshelves and were not converted into appropriate management policies and infrastructural models. This was largely due to the absence of a central administrative organizational structure, which developed later in the 12th century, following the Second Council of Lateran (1139) when the Papacy asserted its authority to establish administrative control over the organizational church. It was only then that management policies started to be framed and institutional structures enacted to deal more appropriately with cCSA from the 14th to 20th centuries. Despite this, RCC developed a culture of secrecy using clandestine organizational management models and institutional laws prescribed in 1568, 1622, 1741, 1866, 1922, and 1962 which aimed to manage cCSA. The current study traces reported cCSA as far back as the first century and critically examines the organizational laws, and institutional policies developed by RCC to address clerical sexual misconduct up to the end of the 19th century.

Traumatic brain injury can cause extensive damage to the white matter (WM) of the brain. These disruptions can be especially damaging in children, whose brains are still maturing. Diffusion magnetic resonance imaging (dMRI) is the most commonly used method to assess WM organization, but it has limited resolution to differentiate causes of WM disruption. Magnetic resonance spectroscopy (MRS) yields spectra showing the levels of neurometabolites that can indicate neuronal/axonal health, inflammation, membrane proliferation/turnover, and other cellular processes that are on-going post-injury. Previous analyses on this dataset revealed a significant division within the msTBI patient group, based on interhemispheric transfer time (IHTT); one subgroup of patients (TBI-normal) showed evidence of recovery over time, while the other showed continuing degeneration (TBI-slow). We combined dMRI with MRS to better understand WM disruptions in children with moderate-severe traumatic brain injury (msTBI). Tracts with poorer WM organization, as shown by lower FA and higher MD and RD, also showed lower N-acetylaspartate (NAA), a marker of neuronal and axonal health and myelination. We did not find lower NAA in tracts with normal WM organization. Choline, a marker of inflammation, membrane turnover, or gliosis, did not show such associations. We further show that multi-modal imaging can improve outcome prediction over a single modality, as well as over earlier cognitive function measures. Our results suggest that demyelination plays an important role in WM disruption post-injury in a subgroup of msTBI children and indicate the utility of multi-modal imaging.

The current report aims to clarify the practices and implicit intentions of the Holy See in addressing child sexual abuse (CSA) by clerics in the twenty-first century. Church investigation reports, United Nations reports, press coverage, and academic literature were explored to understand the relationship between Church intention and practice. Various types of literature highlight the Roman Catholic Church (RCC) as an organization that is self-referential and self-defensive, with the implicit aim of protecting the reputation of RCC at the expense of children. Organizational responses are seen as promoting the protection of perpetrating clerics, resulting in the spread of CSA by clergy nationally and globally. Recommendations are made for the introduction of modern-day management practices in RCC, filtered through a faithful understanding of gospel principles. The authors suggest that the United Nations may be an important vehicle for fostering leverage for change in RCC child protection practice.

Objective:To examine longitudinal trajectories of white matter organization in pediatric moderate/severe traumatic brain injury (msTBI) over a 12-month period.Methods:We studied 21 children (16 M/5 F) with msTBI, assessed 2–5 months postinjury and again 13–19 months postinjury, as well as 20 well-matched healthy control children. We assessed corpus callosum function through interhemispheric transfer time (IHTT), measured using event-related potentials, and related this to diffusion-weighted MRI measures of white matter (WM) microstructure. At the first time point, half of the patients with TBI had significantly slower IHTT (TBI-slow-IHTT, n = 11) and half were in the normal range (TBI-normal-IHTT, n = 10).Results:The TBI-normal-IHTT group did not differ significantly from healthy controls, either in WM organization in the chronic phase or in the longitudinal trajectory of WM organization between the 2 evaluations. In contrast, the WM organization of the TBI-slow-IHTT group was significantly lower than in healthy controls across a large portion of the WM. Longitudinal analyses showed that the TBI-slow-IHTT group experienced a progressive decline between the 2 evaluations in WM organization throughout the brain.Conclusions:We present preliminary evidence suggesting a potential biomarker that identifies a subset of patients with impaired callosal organization in the first months postinjury who subsequently experience widespread continuing and progressive degeneration in the first year postinjury.

Traumatic brain injury (TBI) is a significant public health concern, and can be especially disruptive in children, derailing on-going neuronal maturation in periods critical for cognitive development. There is considerable heterogeneity in post-injury outcomes, only partially explained by injury severity. Understanding the time course of recovery, and what factors may delay or promote recovery, will aid clinicians in decision-making and provide avenues for future mechanism-based therapeutics. We examined regional changes in brain volume in a pediatric/adolescent moderate-severe TBI (msTBI) cohort, assessed at two time points. Children were first assessed 2–5 months post-injury, and again 12 months later. We used tensor-based morphometry (TBM) to localize longitudinal volume expansion and reduction. We studied 21 msTBI patients (5 F, 8–18 years old) and 26 well-matched healthy control children, also assessed twice over the same interval. In a prior paper, we identified a subgroup of msTBI patients, based on interhemispheric transfer time (IHTT), with significant structural disruption of the white matter (WM) at 2–5 months post injury. We investigated how this subgroup (TBI-slow, N = 11) differed in longitudinal regional volume changes from msTBI patients (TBI-normal, N = 10) with normal WM structure and function. The TBI-slow group had longitudinal decreases in brain volume in several WM clusters, including the corpus callosum and hypothalamus, while the TBI-normal group showed increased volume in WM areas. Our results show prolonged atrophy of the WM over the first 18 months post-injury in the TBI-slow group. The TBI-normal group shows a different pattern that could indicate a return to a healthy trajectory., Highlights • Studied differences in regional volume change longitudinally between TBI and control. • Found decreases in TBI group in white matter, gray matter and subcortical regions • Changes in regional volume were associated with changes in cognitive performance. • Compared subgroups within TBI group based on interhemispheric transfer time (IHTT) • TBI-slow group seems to drive TBI vs. control results, show prolonged degeneration.

BackgroundPTSD and depression commonly co-occur and have been associated with smaller hippocampal volumes compared to healthy and trauma-exposed controls. However, the hippocampus is heterogeneous, with subregions that may be uniquely affected in individuals with PTSD and depression.MethodsWe used random effects regressions and a harmonized neuroimaging protocol based on FreeSurfer (v6.0) to identify sub-structural hippocampal markers of current PTSD (C-PTSD), depression, and the interaction of these conditions across 31 cohorts worldwide (N=3,115;Mage=38.9±13.9 years). Secondary analyses tested these associations by sex and after modeling the simultaneous effects of remitted PTSD, childhood trauma, mild traumatic brain injury, and alcohol use disorder.ResultsA significant negative main effect of depression (n=800, vs. no depression, n=1456) was observed in the hippocampal tail (ß=−0.13) and CA1 (ß=−0.09) after adjusting for covariates and multiple testing (adjusted p’s (q)=0.028). A main effect of C-PTSD (n=1042 vs. control, n=1359) was not significant, but an interaction between C-PTSD and depression was significant in the CA1 (ß=−0.24, q=0.044). Pairwise comparisons revealed significantly smaller CA1 volumes in individuals with C-PTSD+Depression than controls (ß=−0.12, q=0.012), C-PTSD-only (ß=−0.17, q=0.001), and Depression-only (ß=−0.18, q=0.023). Follow-up analyses revealed sex effects in the hippocampal tail of depressed females, and an interaction effect of C-PTSD and depression in the fimbria of males.ConclusionsCollectively our results suggest that depression is a stronger predictor of hippocampal volumetry than PTSD, particularly in the CA1, and provide compelling evidence of more pronounced hippocampal phenotypes in comorbid PTSD and depression compared to either condition alone.

The current study considers reasons for the consistent focus of academic research and mainstream media on clerical child sexual abuse (cCSA) largely within the Roman Catholic Church, seeming to ignore cCSA in other Christian denominations and religions. This study includes an analysis of traditional mainstream media and case reports on cCSA in non-Catholic churches and other religious faiths. The authors highlight the inadequacy of research and identify a wide range of gaps in this so far neglected area. Commonalities of cCSA and institutional responses are identified across denominations and religions, and reasons for the apparent over focus on the Catholic Church are discussed. The mains potential reasons identified were: (a) the centralized nature of the Church's universal organizational stature and management structure; (b) the anti-Catholic political and media bias in Protestant-dominated developed countries; (c) secular legal systems with access to powerful lawyers and insurance companies to locate responsibility at organizational level in order to seek compensation and finally; (d) the organized institutional power exercisable by respective bishops to silence victims. Future research needs to move beyond analysis of existing academic literature, press, and case review reports to comparative empirical studies across denominations and religions.

Large scale adoption of novel solutions is the ultimate goal in many domains, and numerous factors need to be addressed to reach that success. This process is even more challenging when those systems are intended for human operators. Not only the technical performance of the system needs to be of the desired quality, but a range of other characteristics also gets scrutinized as well. The design and development of learning and training solutions will be encumbered by additional factors characteristic of learning and training processes. Current adoption of learning and training solutions is far from the desired state: the extent to which learning and training solutions became an every-day practice of their intended users is still much lower than the investment made in this domain. Our research suggests that a good part of that blame can be laid on elements of system design that did not match users’ needs, skills, and expectations. In this paper, we report the results and lessons learned in multiple efforts focused on design and prototyping of a diverse set of training systems that used both immersive and non-immersive virtual reality technologies and a variety of 3D user interface solutions. Approaches discussed and suggested in this paper are equally applicable to the design of systems intended for other human activities in both civilian and military domains.

Memory and related cognitive functions are progressively impaired in a subgroup of individuals experiencing childhood adversity and stress. However, it is not possible to identify vulnerable individuals early, a crucial step for intervention. In this study, high-resolution magnetic resonance imaging (MRI) and intra-hippocampal diffusion tensor imaging (DTI) were employed to examine for structural signatures of cognitive adolescent vulnerabilities in a rodent model of early-life adversity. These methods were complemented by neuroanatomical and functional assessments of hippocampal network integrity during adolescence, adulthood and middle-age. The high-resolution MRI identified selective loss of dorsal hippocampal volume, and intra-hippocampal DTI uncovered disruption of dendritic structure, consistent with disrupted local connectivity, already during late adolescence in adversity-experiencing rats. Memory deteriorated over time, and stunting of hippocampal dendritic trees was apparent on neuroanatomical analyses. Thus, disrupted hippocampal neuronal structure and connectivity, associated with cognitive impairments, are detectable via non-invasive imaging modalities in rats experiencing early-life adversity. These high-resolution imaging approaches may constitute promising tools for prediction and assessment of at-risk individuals in the clinic. © 2016 Wiley Periodicals, Inc.

Following armed conflict in the North Waziristan Agency, a mental health and psychosocial support initiative was launched for internally displaced persons in Bannu, Pakistan. This was convened by volunteer mental health professionals, in collaboration with a variety of agencies (provincial government, military, humanitarian agencies) in a security compromised region. As part of the initiative, monthly camps were held for a period of six months. Mental health needs were assessed. A multidisciplinary team (psychiatrists, psychologists, psychiatric nurses and psychosocial workers) offered mental health care to 680 people who attended the camps, of which 28% were under the age of 18 years old. Twenty-one percent returned for follow-up, while others were followed-up in the community by psychosocial teams. Estimates of common mental disorders were found. Both pharmacological and psychological treatments were offered, according to existing guidelines. Active efforts were made to conduct holistic assessments and avoid a pure biomedical approach. This also provided an opportunity for training non specialist staff and led to formal (World Health Organization) mental health gap action plan training for primary care staff. © 2016 War Trauma Foundation, Diemen, The Netherlands Language: en

Secondhand smoke exposure is a major public health risk that is especially harmful to the developing brain, but it is unclear if early exposure affects brain structure during middle age and older adulthood. Here we analyzed brain MRI data from the UK Biobank in a population-based sample of individuals (ages 44-80) who were exposed (n=2,510) or unexposed (n=6,079) to smoking around birth. We used robust statistical models, including quantile regressions, to test the effect of perinatal smoke exposure (PSE) on cortical surface area (SA), thickness, and subcortical volume. We hypothesized that PSE would be associated with cortical disruption in primary sensory areas compared to unexposed (PSE-) adults. After adjusting for multiple comparisons, SA was significantly lower in the pericalcarine (PCAL), inferior parietal (IPL), and regions of the temporal and frontal cortex of PSE+adults; these abnormalities were associated with increased risk for several diseases, including circulatory and endocrine conditions. Sensitivity analyses conducted in a hold-out group of healthy participants (exposed, n=109, unexposed, n=315) replicated the effect of PSE on SA in the PCAL and IPL. Collectively our results show a negative, long term effect of PSE on sensory cortices that may increase risk for disease later in life.

Secondhand smoke exposure is a major public health risk that is especially harmful to the developing brain, but it is unclear if early exposure affects brain structure during middle age and older adulthood. Here we analyzed brain MRI data from the UK Biobank in a population-based sample of individuals (ages 44–80) who were exposed (n = 2510) or unexposed (n = 6079) to smoking around birth. We used robust statistical models, including quantile regressions, to test the effect of perinatal smoke exposure (PSE) on cortical surface area (SA), thickness, and subcortical volumes. We hypothesized that PSE would be associated with cortical disruption in primary sensory areas compared to unexposed (PSE−) adults. After adjusting for multiple comparisons, SA was significantly lower in the pericalcarine (PCAL), inferior parietal (IPL), and regions of the temporal and frontal cortex of PSE+ adults; these abnormalities were associated with increased risk for several diseases, including circulatory and endocrine conditions. Sensitivity analyses conducted in a hold-out group of healthy participants (exposed, n = 109, unexposed, n = 315) replicated the effect of PSE on SA in the PCAL and IPL. Collectively our results show a negative, long term effect of PSE on sensory cortices that may increase risk for disease later in life.

Background: Cancer is a far-reaching and lethal but curable disease. Researchers have investigated numerous anticancer agents with only a few commercially available effective drugs which are very costly. Objective: Herein, we report the synthesis , characterization and anti cancer assays of a series of novel dithiocarbamates derivatives. Methods: All compounds were synthesized from different secondary amines and substituted benzyl chlorides in a single step. The structures of newly synthesized dithiocarbamate derivatives were confirmed by spectroscopic techniques (IR, NMR and HR-MS). Results: The synthesized compounds showed a significant anti-proliferative effect in cancer cells (HeLa) with the maximum inhibitory activity of compound SHD-2 with an IC50 = 0.31 ± 0.09 μM. However, the same compound exhibited 19.2% inhibition towards Baby Hamster Kidney fibroblasts (BHK-21), normal cell lines. Moreover, quantification of cellular DNA by flow cytometry for the evaluation of pro-apoptotic activity in HeLa cells demonstrates that arrest in cell cycle along with apoptosis advance towards drug cytotoxicity. However, molecular docking studies of the potent compound suggested that it binds to the major groove of the DNA. Conclusion: The cytotoxic and pro-apoptotic potential of the potent inhibitor may be further investigated in the animal models to advance their anti-cancer prospective.

[Image: see text] COVER ILLUSTRATION The combined predictive power of multi‐modal imaging in pediatric traumatic brain injury. Colors correspond to the amount of variance (R‐squared) in cognitive function explained by a combination of modalities, with red being the largest amount of variance. Fractional anisotropy and n‐acetylaspartate were averaged within tracts and combined with inter hemispheric transfer time collected using visual event‐related potential 2‐5 months post‐injury to explain 25‐38% of the variance in cognitive function 12 months later. The imaging variables from the frontal corpus callous (in red) were most predictive of cognitive function.

This paper investigates and analyses the nature of errors occurring in Optical Character Recognition (OCR) for Arabic-like scripts. Existing research on the area of OCR for Arabic-like scripts often focuses on achieving the best performance in terms of character error rates. Only little effort targets at the analysis of the nature of these errors (anomalies) that may occur. One such important anomaly is Space Anomaly. This anomaly is due to the presence of breaker characters that are an essential part of Arabic-like scripts. The spaces introduced by breaker characters are not depicted in the ground truth making it hard for OCR to generalize. The OCR model either learns to inhibit the original spaces or to generate extra spaces at places where they are not correct. Due to this confusion, the rendering looks sub-optimal. This analyses and removes space anomalies. We present a joint approach that does not only perform OCR but also handles the space anomalies in a robust manner, hence significantly outperforming the state-of-the-art. Although the implication of the work is shown by improved character recognition rate, the impact of this research is much higher in terms of the correctness of the OCR for useful purposes, especially for rendering. The claim is supported by empirical evaluation and it is shown that the proposed approach achieved the best results.

Antioxidant properties of grape (GSE) and papaya seed extracts (PSE) were tested in vitro at varied concentrations and growth inhibition were seen against gram positive and gram negative bacteria by disc diffusion method. The results revealed that GSE contain four times higher phenolic and six folds higher flavonoid content than PSE. The antioxidant properties of GSE and PSE showed dose dependent activities and were comparatively much higher in GSE. Linoleic acid model of GSE and PSE displayed 67.67 and 46.43 % of inhibition respectively at 500 mg/L. The effect of dip treatment by GSE and PSE at a concentration of 500 and 1000 mg/L respectively on the quality changes of Indian mackerel (Rastrelliger kanagurta) in iced condition were assessed using chemical, microbiological and sensory parameters along with chilled whole control (CWC). The inhibition of primary and secondary lipid oxidation products by GSE at 500 mg/L was comparable to BHT at 200 mg/L. GSE exhibited higher antimicrobial activity on gram-positive strains compared to PSE and reduced the formation of volatile bases significantly. On the day of sensory rejection for CWC, the formation of trimethylamine and total volatile base nitrogen were reduced by 32.27 and 31.85 % in GSE samples and 19.01 and 24.70 % in PSE samples respectively. The dip treatment of GSE increased the shelflife of mackerel up to 15 days, PSE by 12 and 9 days for CWC during ice storage. Therefore, it can be concluded that, GSE can be used as a promising natural preservative and a substitute to the synthetic counterparts.

Character recognition has been widely used since its inception in applications involved processing of scanned or camera-captured documents. There exist multiple scripts in which the languages are written. The scripts could broadly be divided into cursive and non-cursive scripts. The recurrent neural networks have been proved to obtain state-of-the-art results for optical character recognition. We present a thorough investigation of the performance of recurrent neural network (RNN) for cursive and non-cursive scripts. We employ bidirectional long short-term memory (BLSTM) networks, which is a variant of the standard RNN. The output layer of the architecture used to carry out our investigation is a special layer called connectionist temporal classification (CTC) which does the sequence alignment. The CTC layer takes as an input the activations of LSTM and aligns the target labels with the inputs. The results were obtained at the character level for both cursive Urdu and non-cursive English scripts are significant and suggest that the BLSTM technique is potentially more useful than the existing OCR algorithms.

Objectives: We sought to determine the role of obesity and insulin resistance (IR) in the pathogenesis of inflammation in metabolic syndrome (MetS). Methods: Our study included 100 patients with MetS and 100 age and gender matched control patients who attended a tertiary care laboratory in Rawalpindi, Pakistan. Anthropometric data was obtained including height and weight to calculate body mass index. A record of patient’s blood pressure (BP), waist circumference (WC) and hip circumference (HC) was made. Biochemical analysis included measurements of fasting glucose, triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), insulin, and high-sensitivity C reactive protein (hsCRP). IR was determined by the homeostasis mode assessment insulin resistance (HOMA-IR) method. Results: The levels of hs-CRP were found to be elevated in all patients with MetS where it correlated significantly with all its components including measures of obesity, fasting insulin and glucose levels, IR, TG and HDL-c. However, on linear regression analysis only WC, fasting insulin, and HOMA-IR remained significantly correlated with hs-CRP. Conclusion: MetS is a condition characterized by chronic low-grade inflammation, which arises because of increased abdominal adiposity and IR. Large multicenter studies are needed to gain insight into its pathogenesis and derive treatment strategies.

Outcome after a traumatic brain injury (TBI) is quite variable, and this variability is not solely accounted for by severity or demographics. Identifying sub-groups of patients who recover faster or more fully will help researchers and clinicians understand sources of this variability, and hopefully lead to new therapies for patients with a more prolonged recovery profile. We have previously identified two subgroups within the pediatric TBI patient population with different recovery profiles based on an ERP-derived (event-related potential) measure of interhemispheric transfer time (IHTT). Here we examine structural network topology across both patient groups and healthy controls, focusing on the ‘rich-club’ - the core of the network, marked by high degree nodes. These analyses were done at two points post-injury - 2-5 months (post-acute), and 13-19 months (chronic). In the post-acute time-point, we found that the TBI-slow group, those showing longitudinal degeneration, showed hyperconnectivity within the rich-club nodes relative to the healthy controls, at the expense of local connectivity. There were minimal differences between the healthy controls and the TBI-normal group (those patients who show signs of recovery). At the chronic phase, these disruptions were no longer significant, but closer analysis showed that this was likely due to the loss of power from a smaller sample size at the chronic time-point, rather than a sign of recovery. We have previously shown disruptions to white matter (WM) integrity that persist and progress over time in the TBI-slow group, and here we again find differences in the TBI-slow group that fail to resolve over the first year post-injury.

Mild traumatic brain injury (mTBI) is characterized clinically by a closed head injury involving differential or rotational movement of the brain inside the skull. Over 3 million mTBIs occur annually in the United States alone. Many of the individuals who sustain an mTBI go on to recover fully, but around 20% experience persistent symptoms. These symptoms often last for many weeks to several months. The thalamus, a structure known to serve as a global networking or relay system for the rest of the brain, may play a critical role in neurorehabiliation and its integrity and connectivity after injury may also affect cognitive outcomes. To examine the thalamus, conventional tractography methods to map corticothalamic pathways with diffusion-weighted MRI (DWI) lead to sparse reconstructions that may contain false positive fibers that are anatomically inaccurate. Using a specialized method to zero in on corticothalamic pathways with greater robustness, we noninvasively examined corticothalamic fiber projections using DWI, in 68 service members. We found significantly lower fractional anisotropy (FA), a measure of white matter microstructural integrity, in pathways projecting to the left pre- and postcentral gyri – consistent with sensorimotor deficits often found post-mTBI. Mapping of neural circuitry in mTBI may help to further our understanding of mechanisms underlying recovery post-TBI.

This work presents state-of-the-art results on one of the complex datasets; known as KHATT. The KHATT dataset shows complex patterns for Arabic handwritten text. We have achieved better performance in terms of Character Recognition by implementing the most successful deep learning approach based on Long Short-Term Memory (LSTM) networks. Connectionist Temporal Classification (CTC) is used as a final layer to align the predicted labels according to the most probable path. The application of MDLSTM scans text-lines in all direction to cover fine inflammation in horizontal and vertical direction. Further, we apply pre-processing on text-lines to prune extra white regions, and de-skew the text lines for accurate height normalization. The deep learning and pre-processing allow us to improve results from 46.13% to 75.8%.

Many languages use Arabic script for written communication either in basic or augmented form. These languages include Urdu, Pashto, Persian, etc. As the primary characters are shared among all these languages, it is possible to take advantage of the visual similarities for Optical Character Recognition (OCR). OCR models optimized for individual languages have been proposed. However, to the best of our knowledge, there is no attempt to develop a single system for more than one language. The contributions of the presented work are: First, it investigates the effect on the recognition accuracy when different languages are combined (A pioneering study). Second, it introduces publicly available synthetic datasets for Arabic and Pashto languages for experimental purposes. Third, this paper provides statistical analysis as clues for transfer learning concerning OCR systems for Arabic, Urdu, and Pashto languages.

Tables are an easy way to represent information in a structural form. Table recognition is important for the extraction of such information from document images. Usually, modern OCR systems provide textual information coming from tables without recognizing actual table structure. However, recognition of table structure is important to get the contextual meaning of the contents. Table structure recognition in heterogeneous documents is challenging due to a variety of table layouts. It becomes harder where no physical rulings are present in a table. This work proposes a novel learning based methodology for the recognition of table contents in heterogeneous document images. Textual contents of documents are classified as table or non-table elements using a pre-trained neural network model. The output of the neural network is further enhanced by applying a contextual post processing on each element to correct the classifications errors if any. The system is trained using a subset of UNLV and UW3 document images and depicted more than 97% accuracy on a test set in detection of table and non-table elements.

Neurodegenerative disorders are characterized by a progressive loss of brain function. Improved precision in mapping the altered brain pathways can provide a deep understanding of the trajectory of decline. We propose a tractometry workflow for conducting group statistical analyses of point-wise microstructural measures along white matter fasciculi to identify patterns of abnormalities associated with disease. We combined state-of-the-art tools including fiber registration, tract simplification and fiber matching for accurate point-wise statistical analyses across populations. We test the utility of this method by identifying group differences between Parkinson’s disease (PD) patients and healthy controls. We find statistically significant group differences in diffusion MRI derived measures along the anterior thalamic radiations (ATR), corticospinal tract (CST) and regions of the corpus callosum (CC). These pathways are essential for motor control systems within cortico-cortical and cortico-subcortical brain networks. Moreover, the reported pathological changes were not widespread but rather localized along several tracts. Point-wise tract analyses may therefore offer an advantage in anatomical specificity over traditional methods that assess mean microstructural measures across large regions of interest.

The brain’s white matter fiber tracts are impaired in a range of common and devastating conditions, from Alzheimer’s disease to brain trauma, and in developmental disorders such as autism and neurogenetic syndromes. Many studies now examine the connectivity and microstructure of the brain’s neural pathways, spurring the development of algorithms to extract and measure tracts and fiber bundles. Clustering white matter (WM) fibers, from whole-brain tractography, into anatomically meaningful bundles is still a challenging problem. Existing tract segmentation methods use atlases or regions of interest (ROI) or unsupervised spectral clustering. Even so, atlas-based segmentation does not always partition the brain into a set of recognizable fiber bundles. Deep learning techniques can be applied to automatically segment and cluster white matter fibers. Here we propose a robust approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which we then exploit to cluster WM fibers into bundles. In a range of tests across diverse fiber bundles, we illustrate the accuracy of our method, and its ability to suppress false positive fibers.

Background Cognitive deficit associated with cancer and its treatment is called cancer-related cognitive impairment (CRCI). Increases in cancer survival have made understanding the basis of CRCI more important. CRCI neuroimaging studies have traditionally used dedicated research brain MRIs in breast cancer survivors after chemotherapy with small sample sizes; little is known about other non-central nervous system (CNS) cancers after chemotherapy as well as those not exposed to chemotherapy. However, there may be a wealth of unused data from clinically-indicated MRIs that could be used to study CRCI. Objective Evaluate brain cortical structural differences in those with various non-CNS cancers using clinically-indicated MRIs. Design Cross-sectional. Patients Adult non-CNS cancer and non-cancer control (C) patients who underwent clinically-indicated MRIs. Methods Brain cortical surface area and thickness were measured using 3D T1-weighted images. An age-adjusted linear regression model was used and the Benjamini and Hochberg false discovery rate (FDR) corrected for multiple comparisons. Group comparisons were: cancer cases with chemotherapy (Ch+), cancer cases without chemotherapy (Ch-) and subgroup of lung cancer cases with and without chemotherapy vs C. Results Sixty-four subjects were analyzed: 22 Ch+, 23 Ch- and 19 C patients. Subgroup analysis of 16 lung cancer (LCa) patients was also performed. Statistically significant decreases in either cortical surface area or thickness were found in multiple regions of interest (ROIs) primarily within the frontal and temporal lobes for all comparisons. Effect sizes were variable with the greatest seen in the left middle temporal surface area ROI (Cohen's d -0.690) in the Ch- vs C group comparison. Limitations Several limitations were apparent including a small sample size that precluded adjustment for other covariates. Conclusions Our preliminary results suggest that, in addition to breast cancer, other types of non-CNS cancers treated with chemotherapy may result in brain structural abnormalities. Similar findings also appear to occur in those not exposed to chemotherapy. These results also suggest that there is potentially a wealth of untapped clinical MRIs that could be used for future CRCI studies.

White matter tracts are commonly analyzed in studies of micro-structural integrity and anatomical connectivity in the brain. Over the last decade, it has been an open problem as to how best to cluster white matter fibers, extracted from whole-brain tractography, into anatomically meaningful groups. Some existing techniques use region of interest (ROI) based clustering, atlas-based labeling, or unsupervised spectral clustering. ROI-based clustering is popular for analyzing anatomical connectivity among a set of ROIs, but it does not always partition the brain into recognizable fiber bundles. Here we propose an approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which are then exploited to cluster white matter fibers. To achieve such clustering, we first need to re-parameterize the fibers in an intrinsic space. The clustering is performed in induced parameterized coordinates. To our knowledge, this is one of the first approaches for fiber clustering using deep learning techniques. The results show strong accuracy - on a par with or better than other state-of-the-art methods.

Brain development is a protracted and dynamic process. Many studies have charted the trajectory of white matter development, but here we sought to map these effects in greater detail, based on a large set of fiber tracts automatically extracted from HARDI (high angular resolution diffusion imaging) at 4 tesla. We used autoMATE (automated multi-atlas tract extraction) to extract diffusivity measures along 18 of the brain’s major fiber bundles in 667 young adults, aged 18–30. We examined linear and non-linear age effects on diffusivity measures, pointwise along tracts. All diffusivity measures showed both linear and non-linear age effects. Tracts with the most pronounced age effects were those that connected the temporal lobe to the rest of the brain. Nonlinear age effects were picked up strongly in the anterior corpus callosum and right temporo-parietal tracts.