Comparative Demography of the Spider Mite, Oligonychus afrasiaticus, on four Date Palm Varieties in Southwestern Tunisia

The date palm mite, Oligonychus afrasiaticus (McGregor) (Acari: Tetranychidae), is a serious pest of palm date fruits. Life cycle, fecundity, and longevity of this mite were studied on fruits of four date palms, Phoenix dactylifera L. (Arecales: Arecaceae)(varieties: Deglet Noor, Alig, Kentichi, and Besser), under laboratory conditions at 27 = 1 °C, 60 ± 10% RH. Total development time of immature female was shorter on Deglet Noor fruits than on the other cultivars. O. afrasiaticus on Deglet Noor had the highest total fecundity per female, while low fecundity values occurred on Besser. The comparison of intrinsic rates of natural increase (rm), net reproductive rates (Ro), and the survival rates of immature stage of O. afrasiaticus on the host plants suggests that O. afrasiaticus performs better on Deglet Noor fruits. The mite feeding on Alig showed the lowest intrinsic rate of natural population increase (rm = 0.103 day 1). The estimation of difference in susceptibility of cultivars to O. afrasiaticus is crucial for developing efficient pest control programs. Indeed, less susceptible cultivars can either be left unsprayed or sprayed at low threshold

Hedgehog pathway mutations drive oncogenic transformation in high-risk T-cell acute lymphoblastic leukemia.

The role of Hedgehog signaling in normal and malignant T-cell development is controversial. Recently, Hedgehog pathway mutations have been described in T-ALL, but whether mutational activation of Hedgehog signaling drives T-cell transformation is unknown, hindering the rationale for therapeutic intervention. Here, we show that Hedgehog pathway mutations predict chemotherapy resistance in human T-ALL, and drive oncogenic transformation in a zebrafish model of the disease. We found Hedgehog pathway mutations in 16% of 109 childhood T-ALL cases, most commonly affecting its negative regulator PTCH1. Hedgehog mutations were associated with resistance to induction chemotherapy (P = 0.009). Transduction of wild-type PTCH1 into PTCH1-mutant T-ALL cells induced apoptosis (P = 0.005), a phenotype that was reversed by downstream Hedgehog pathway activation (P = 0.007). Transduction of most mutant PTCH1, SUFU, and GLI alleles into mammalian cells induced aberrant regulation of Hedgehog signaling, indicating that these mutations are pathogenic. Using a CRISPR/Cas9 system for lineage-restricted gene disruption in transgenic zebrafish, we found that ptch1 mutations accelerated the onset of notch1-induced T-ALL (P = 0.0001), and pharmacologic Hedgehog pathway inhibition had therapeutic activity. Thus, Hedgehog-activating mutations are driver oncogenic alterations in high-risk T-ALL, providing a molecular rationale for targeted therapy in this disease

Suppression of HBV by Tenofovir in HBV/HIV coinfected patients : a systematic review and meta-analysis

Background: Hepatitis B coinfection is common in HIV-positive individuals and as antiretroviral therapy has made death due to AIDS less common, hepatitis has become increasingly important. Several drugs are available to treat hepatitis B. The most potent and the one with the lowest risk of resistance appears to be tenofovir (TDF). However there are several questions that remain unanswered regarding the use of TDF, including the proportion of patients that achieves suppression of HBV viral load and over what time, whether suppression is durable and whether prior treatment with other HBV-active drugs such as lamivudine, compromises the efficacy of TDF due to possible selection of resistant HBV strains. Methods: A systematic review and meta-analysis following PRISMA guidelines and using multilevel mixed effects logistic regression, stratified by prior and/or concomitant use of lamivudine and/or emtricitabine. Results: Data was available from 23 studies including 550 HBV/HIV coinfected patients treated with TDF. Follow up was for up to seven years but to ensure sufficient power the data analyses were limited to three years. The overall proportion achieving suppression of HBV replication was 57.4%, 79.0% and 85.6% at one, two and three years, respectively. No effect of prior or concomitant 3TC/FTC was shown. Virological rebound on TDF treatment was rare. Interpretation: TDF suppresses HBV to undetectable levels in the majority of HBV/HIV coinfected patients with the proportion fully suppressed continuing to increase during continuous treatment. Prior treatment with 3TC/FTC does not compromise efficacy of TDF treatment. The use of combination treatment with 3TC/FTC offers no significant benefit over TDF alone

New Mechanics of Traumatic Brain Injury

The prediction and prevention of traumatic brain injury is a very important aspect of preventive medical science. This paper proposes a new coupled loading-rate hypothesis for the traumatic brain injury (TBI), which states that the main cause of the TBI is an external Euclidean jolt, or SE(3)-jolt, an impulsive loading that strikes the head in several coupled degrees-of-freedom simultaneously. To show this, based on the previously defined covariant force law, we formulate the coupled Newton-Euler dynamics of brain's micro-motions within the cerebrospinal fluid and derive from it the coupled SE(3)-jolt dynamics. The SE(3)-jolt is a cause of the TBI in two forms of brain's rapid discontinuous deformations: translational dislocations and rotational disclinations. Brain's dislocations and disclinations, caused by the SE(3)-jolt, are described using the Cosserat multipolar viscoelastic continuum brain model. Keywords: Traumatic brain injuries, coupled loading-rate hypothesis, Euclidean jolt, coupled Newton-Euler dynamics, brain's dislocations and disclinationsComment: 18 pages, 1 figure, Late

Converting simulated total dry matter to fresh marketable yield for field vegetables at a range of nitrogen supply levels

Simultaneous analysis of economic and environmental performance of horticultural crop production requires qualified assumptions on the effect of management options, and particularly of nitrogen (N) fertilisation, on the net returns of the farm. Dynamic soil-plant-environment simulation models for agro-ecosystems are frequently applied to predict crop yield, generally as dry matter per area, and the environmental impact of production. Economic analysis requires conversion of yields to fresh marketable weight, which is not easy to calculate for vegetables, since different species have different properties and special market requirements. Furthermore, the marketable part of many vegetables is dependent on N availability during growth, which may lead to complete crop failure under sub-optimal N supply in tightly calculated N fertiliser regimes or low-input systems. In this paper we present two methods for converting simulated total dry matter to marketable fresh matter yield for various vegetables and European growth conditions, taking into consideration the effect of N supply: (i) a regression based function for vegetables sold as bulk or bunching ware and (ii) a population approach for piecewise sold row crops. For both methods, to be used in the context of a dynamic simulation model, parameter values were compiled from a literature survey. Implemented in such a model, both algorithms were tested against experimental field data, yielding an Index of Agreement of 0.80 for the regression strategy and 0.90 for the population strategy. Furthermore, the population strategy was capable of reflecting rather well the effect of crop spacing on yield and the effect of N supply on product grading

Multi-center MRI prediction models: Predicting sex and illness course in first episode psychosis patients.

Structural Magnetic Resonance Imaging (MRI) studies have attempted to use brain measures obtained at the first-episode of psychosis to predict subsequent outcome, with inconsistent results. Thus, there is a real need to validate the utility of brain measures in the prediction of outcome using large datasets, from independent samples, obtained with different protocols and from different MRI scanners. This study had three main aims: 1) to investigate whether structural MRI data from multiple centers can be combined to create a machine-learning model able to predict a strong biological variable like sex; 2) to replicate our previous finding that an MRI scan obtained at first episode significantly predicts subsequent illness course in other independent datasets; and finally, 3) to test whether these datasets can be combined to generate multicenter models with better accuracy in the prediction of illness course. The multi-center sample included brain structural MRI scans from 256 males and 133 females patients with first episode psychosis, acquired in five centers: University Medical Center Utrecht (The Netherlands) (n=67); Institute of Psychiatry, Psychology and Neuroscience, London (United Kingdom) (n=97); University of São Paulo (Brazil) (n=64); University of Cantabria, Santander (Spain) (n=107); and University of Melbourne (Australia) (n=54). All images were acquired on 1.5-Tesla scanners and all centers provided information on illness course during a follow-up period ranging 3 to 7years. We only included in the analyses of outcome prediction patients for whom illness course was categorized as either "continuous" (n=94) or "remitting" (n=118). Using structural brain scans from all centers, sex was predicted with significant accuracy (89%; p<0.001). In the single- or multi-center models, illness course could not be predicted with significant accuracy. However, when reducing heterogeneity by restricting the analyses to male patients only, classification accuracy improved in some samples. This study provides proof of concept that combining multi-center MRI data to create a well performing classification model is possible. However, to create complex multi-center models that perform accurately, each center should contribute a sample either large or homogeneous enough to first allow accurate classification within the single-center

Nano-mechanical properties of Fe-Mn-Al-C lightweight steels

High Al Low-density steels could have a transformative effect on the light-weighting of steel structures for transportation and achieving the desired properties with the minimum amount of Ni is of great interest from an economic perspective. In this study, the mechanical properties of two duplex low-density steels, Fe-15Mn-10Al-0.8C-5Ni and Fe-15Mn-10Al-0.8C (wt.%) were investigated through nano-indentation and simulation through utilization of ab initio formalisms in Density Functional Theory (DFT) in order to establish the hardness resulting from two critical structural features (ߢ-carbides and B2 intermetallic) as a function of annealing temperature (500 − 1050 ℃) and the addition of Ni. In the Ni-free sample, the calculated elastic properties of kappa-carbides were compared with those of the B2 intermetallic Fe3Al − L12, and the role of Mn in the kappa structure and its elastic properties were studied. The Ni-containing samples were found to have a higher hardness due to the B2 phase composition being NiAl rather than FeAl, with Ni-Al bonds reported to be stronger than the Fe-Al bonds. In both samples, at temperatures of 900 ℃ and above, the ferrite phase contained nano-sized discs of B2 phase, wherein the Ni-containing samples exhibited higher hardness, attributed again to the stronger Ni-Al bonds in the B2 phase. At 700 ℃ and below, the nano-sized B2 discs were replaced by micrometre sized needles of kappa in the Ni-free sample resulting in a lowering of the hardness. In the Ni-containing sample, the entire alpha phase was replaced by B2 stringers, which had a lower hardness than the Ni-Al nano-discs due to a lower Ni content in B2 stringer bands formed at 700 ℃ and below. In addition, the hardness of needle-like kappa-carbides formed in alpha phase was found to be a function of Mn content. Although it was impossible to measure the hardness of cuboid kappa particles in gamma phase because of their nano-size, the hardness value of composite phases, e.g. gamma + kappa was measured and reported. All the hardness values were compared and rationalized by bonding energy between different atoms

Experience and Challenges from Clinical Trials with Malaria Vaccines in Africa.

Malaria vaccines are considered amongst the most important modalities for potential elimination of malaria disease and transmission. Research and development in this field has been an area of intense effort by many groups over the last few decades. Despite this, there is currently no licensed malaria vaccine. Researchers, clinical trialists and vaccine developers have been working on many approached to make malaria vaccine available.African research institutions have developed and demonstrated a great capacity to undertake clinical trials in accordance to the International Conference on Harmonization-Good Clinical Practice (ICH-GCP) standards in the last decade; particularly in the field of malaria vaccines and anti-malarial drugs. This capacity is a result of networking among African scientists in collaboration with other partners; this has traversed both clinical trials and malaria control programmes as part of the Global Malaria Action Plan (GMAP). GMAP outlined and support global strategies toward the elimination and eradication of malaria in many areas, translating in reduction in public health burden, especially for African children. In the sub-Saharan region the capacity to undertake more clinical trials remains small in comparison to the actual need.However, sustainability of the already developed capacity is essential and crucial for the evaluation of different interventions and diagnostic tools/strategies for other diseases like TB, HIV, neglected tropical diseases and non-communicable diseases. There is urgent need for innovative mechanisms for the sustainability and expansion of the capacity in clinical trials in sub-Saharan Africa as the catalyst for health improvement and maintained

Phenotypic redshifts with self-organizing maps: A novel method to characterize redshift distributions of source galaxies for weak lensing

Wide-field imaging surveys such as the Dark Energy Survey (DES) rely on coarse measurements of spectral energy distributions in a few filters to estimate the redshift distribution of source galaxies. In this regime, sample variance, shot noise, and selection effects limit the attainable accuracy of redshift calibration and thus of cosmological constraints. We present a new method to combine wide-field, few-filter measurements with catalogs from deep fields with additional filters and sufficiently low photometric noise to break degeneracies in photometric redshifts. The multi-band deep field is used as an intermediary between wide-field observations and accurate redshifts, greatly reducing sample variance, shot noise, and selection effects. Our implementation of the method uses self-organizing maps to group galaxies into phenotypes based on their observed fluxes, and is tested using a mock DES catalog created from N-body simulations. It yields a typical uncertainty on the mean redshift in each of five tomographic bins for an idealized simulation of the DES Year 3 weak-lensing tomographic analysis of $\sigma_{\Delta z} = 0.007$, which is a 60% improvement compared to the Year 1 analysis. Although the implementation of the method is tailored to DES, its formalism can be applied to other large photometric surveys with a similar observing strategy.Comment: 24 pages, 11 figures; matches version accepted to MNRA