Microwave remote sensing of physically buried objects in the Negev Desert: implications for environmental research

We report remote detections of physically buried specularly reflecting objects using microwave radar at two sites: Ashalim and Tseelim in the northern region of the Negev Desert, Israel. These detections provide confirmation that microwave subsurface remote sensing is a genuine phenomenon. At Ashalim, a scatterometer operating in the P-band (441 MHz, 68 cm) was mounted on a cherry picker truck at a height of 8 m and used to detect two triangular aluminum mesh reflectors (forming a 1-m square area reflector) buried down to a depth of 8 cm in dry sand. At Tseelim, the same scatterometer was mounted on an airplane flying at an altitude of 70 m and used to detect 1-m square aluminum reflectors (each one submerged at a different location along the airplane flight path) buried down to a depth of 20 cm. The experimental results compare favorably with a theoretical model that incorporates radar absorption effects arising in the sandy subsurface layer and radar interference effects arising from phase differences between reflections from the surface and buried reflector. The theoretical modeling also predicts the detection of a subsurface reflector down to a depth of about 4.4 m. This experiment and the associated modeling approach is the first of a series of planned experiments, which we outline for the detection and the theoretical evaluation of buried reflectors using remote microwave and VHF radar. We identify potential subject areas for environmental research.     

The Emerging Role of Venetoclax-Based Treatments in Acute Lymphoblastic Leukemia

Venetoclax, a B-cell lymphoma (BCL-2) inhibitor, in combination with hypomethylating agents has become the new standard of care in elderly and unfit patients with acute myeloid leukemia, with significantly improved overall survival and quality of life. Studies of venetoclax combined with high-dose chemotherapy are emerging with evidence of higher rates of molecular remission. Recently, a growing number of publications bring forth the use of venetoclax in patients with acute lymphoblastic leukemia (ALL). In the current review, we present the biological rationale of BCL-2 inhibition in ALL, how the interplay of BH3 proteins modulate the response and the current clinical experience with various combinations.     

Cytoskeleton Elements Contribute to Prion Peptide-Induced Endothelial Barrier Breakdown in a Blood–Brain Barrier In Vitro System

The mechanisms involved in the interaction of PrP 106-126, a peptide corresponding to the prion protein amyloidogenic region, with the blood–brain barrier (BBB) were studied. PrP 106-126 treatment that was previously shown to impair BBB function, reduced cAMP levels in cultured brain endothelial cells, increased nitric oxide (NO) levels, and changed the activation mode of the small GTPases Rac1 (inactivation) and RhoA (activation). The latter are well established regulators of endothelial barrier properties that act via cytoskeletal elements. Indeed, liquid chromatography-mass spectrometry (LC-MS)-based proteomic profiling study revealed extensive changes in expression of cytoskeleton-related proteins. These results shed light on the nature of the interaction between the prion peptide PrP 106-126 and the BBB and emphasize the importance of the cytoskeleton in endothelium response to prion- induced stress.     

Separation of Transparent Layers using Focus

Consider situations where the depth at each point in the scene is multi-valued, due to the presence of a virtual image semi-reflected by a transparent surface. The semi-reflected image is linearly superimposed on the image of an object that is behind the transparent surface. A novel approach is proposed for the separation of the superimposed layers. Focusing on either of the layers yields initial separation, but crosstalk remains. The separation is enhanced by mutual blurring of the perturbing components in the images. However, this blurring requires the estimation of the defocus blur kernels. We thus propose a method for self calibration of the blur kernels, given the raw images. The kernels are sought to minimize the mutual information of the recovered layers. Autofocusing and depth estimation in the presence of semi-reflections are also considered. Experimental results are presented.     

Sleeved co-clustering of lagged data

The paper focuses on mining clusters that are characterized by a lagged relationship between the data objects. We call such clusters lagged co-clusters. A lagged co-cluster of a matrix is a submatrix determined by a subset of rows and their corresponding lag over a subset of columns. Extracting such subsets may reveal an underlying governing regulatory mechanism. Such a regulatory mechanism is quite common in real-life settings. It appears in a variety of fields: meteorology, seismic activity, stock market behavior, neuronal brain activity, river flow, and navigation, but a limited list of examples. Mining such lagged co-clusters not only helps in understanding the relationship between objects in the domain, but assists in forecasting their future behavior. For most interesting variants of this problem, finding an optimal lagged co-cluster is NP-complete problem. We present a polynomial-time Monte-Carlo algorithm for mining lagged co-clusters. We prove that, with fixed probability, the algorithm mines a lagged co-cluster which encompasses the optimal lagged co-cluster by a maximum 2 ratio columns overhead and completely no rows overhead. Moreover, the algorithm handles noise, anti-correlations, missing values, and overlapping patterns. The algorithm is extensively evaluated using both artificial and real-world test environments. The first enable the evaluation of specific, isolated properties of the algorithm. The latter (river flow and topographic data) enable the evaluation of the algorithm to efficiently mine relevant and coherent lagged co-clusters in environments that are temporal, i.e., time reading data and non-temporal.     

Self‐Assembling,Bioinspired Wax Crystalline Surfaces with Time‐Dependent Wettability

One of the most fascinating properties of materials in nature is the superhydrophobic and self‐cleaning capabilities of different plant surfaces. This is usually achieved by the hydrophobic cuticles that are made of cutin and contain wax crystals both within them and on their surfaces. Here, bioinspired n‐hexatriacontane wax films are deposited via thermal evaporation and it is shown that the surface evolves in time via self‐assembly. This leads to a dramatic change in the wetting properties with a transition from hydrophobic to superhydrophobic characteristics, which takes place within several days at room temperature. This phenomenon is investigated and strain‐induced recrystallization is proposed to be the mechanism for it. This work could become the basis for the inspiration and production of tuned, time‐dependant, temperature‐sensitive, variable‐wettability surfaces.     

From data to knowledge: The interaction between data management systems in educational institutions and the delivery of quality education

No school is an island; it is a part of a continuum or a pipeline of institutions which together form an educational pipeline through which groups of students pass. To turn a body of data into useful information for knowledge-based decision-making at any level, data must be collected, organised, analysed and reflected upon. The purpose of this paper is to discuss how schools and other educational institutions can not only collect better data but learn how to transform that data so that the information held within can be effectively shared among all stakeholders. This process will help to ensure that the school and the entire education system provide a more seamless and effective educational pipeline for students, and ultimately improve the quality of education delivered in the country as a whole.     

TEG—a hybrid approach to information extraction

This paper describes a hybrid statistical and knowledge-based information extraction model, able to extract entities and relations at the sentence level. The model attempts to retain and improve the high accuracy levels of knowledge-based systems while drastically reducing the amount of manual labour by relying on statistics drawn from a training corpus. The implementation of the model, called TEG (trainable extraction grammar), can be adapted to any IE domain by writing a suitable set of rules in a SCFG (stochastic context-free grammar)-based extraction language and training them using an annotated corpus. The system does not contain any purely linguistic components, such as PoS tagger or shallow parser, but allows to using external linguistic components if necessary. We demonstrate the performance of the system on several named entity extraction and relation extraction tasks. The experiments show that our hybrid approach outperforms both purely statistical and purely knowledge-based systems, while requiring orders of magnitude less manual rule writing and smaller amounts of training data. We also demonstrate the robustness of our system under conditions of poor training-data quality. Ronen Feldman is a senior lecturer at the Mathematics and Computer Science Department of Bar-Ilan University in Israel, and the Director of the Data Mining Laboratory. He received his B.Sc. in Math, Physics and Computer Science from the Hebrew University, M.Sc. in Computer Science from Bar-Ilan University, and his Ph.D. in Computer Science from Cornell University in NY. He was an Adjunct Professor at NYU Stern Business School. He is the founder of ClearForest Corporation, a Boston based company specializing in development of text mining tools and applications. He has given more than 30 tutorials on next mining and information extraction and authored numerous papers on these topics. He is currently finishing his book “The Text Mining Handbook” to the published by Cambridge University Press. Benjamin Rosenfeld is a research scientist at ClearForest Corporation. He received his B.Sc. in Mathematics and Computer Science from Bar-Ilan University. He is the co-inventor of the DIAL information extraction language. Moshe Fresko is finalizing his Ph.D. in Computer Science Department at Bar-Ilan University in Israel. He received his B.Sc. in Computer Engineering from Bogazici University, Istanbul/Turkey on 1991, and M.Sc. on 1994. He is also an adjunct lecturer at the Computer Science Department of Bar-Ilan University and functions as the Information-Extraction Group Leader in the Data Mining Laboratory.