Cancer prediction using graph-based gene selection and explainable classifier

Several Artificial Intelligence-based models have been developed for cancer prediction. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered prediction and the potential future of machine-centered cancer prediction. In this study, an efficient and effective model is developed for gene selection and cancer prediction. Moreover, this study proposes an artificial intelligence decision system to provide physicians with a simple and human-interpretable set of rules for cancer prediction. In contrast to previous deep learning-based cancer prediction models, which are difficult to explain to physicians due to their black-box nature, the proposed prediction model is based on a transparent and explainable decision forest model. The performance of the developed approach is compared to three state-of-the-art cancer prediction including TAGA, HPSO and LL. The reported results on five cancer datasets indicate that the developed model can improve the accuracy of cancer prediction and reduce the execution time

Surface-Modified Graphene for Mid-Infrared Detection

In this chapter, morphology variation and electronic structure in a surface-modified graphene are demonstrated by both calculation and experimental results. The results indicate that the band structure and morphology of modified graphene sheets are altered because of changing in the type of hybridization of carbon atoms in the graphene sheet. Accordingly, the band gap of graphene can be tuned by surface modification using organic molecules. Then, modified graphene is used for fabrication of infrared detectors. The properties of unmodified graphene photodetectors were also measured so as to compare with modified graphene photodetectors. The results demonstrate that modification of graphene using organic ligands improved the detection parameters such as fast response time, electrical stability and low dark current. Moreover, the sensitivity of photodetectors based on modified graphene was significantly improved

Treatment of Kidney Stones Using Extracorporeal Shock Wave Lithotripsy (ESWL) and Double-J Stent in Infants

Background. Extracorporeal shock wave lithotripsy (ESWL) has progressively acquired popularity as being the gold standard treatment for upper urinary tract lithiasis in infants since 1980. Our aim was to evaluate the outcome of ESWL for kidney stones and the use of double-J stent in infants. Material and Methods. A prospective clinical trial study performed on 50 infants with renal calculi at pelvic admitted in the Urology ward of Shafa Hospital, Sari, Iran, between 2001 and 2010. Main outcome measure of our study was clearing stones after one or more consecutive sessions of ESWL. Results. The study included 50 patients with renal calculi at pelvic. Among them, there were 35 (70%) boys and 15 (30%) girls with the age ranging from 1 to 13 months (mean of 7 month ± 3 days). All of them were treated by standard ESWL using Simons Lithostor plus machine. The stone sizes ranged from 6 mm to 22 mm. Double-J stents were placed in 11 infants (22%) with stones larger than 13 mm. Most of the patients required only one ESWL session. Conclusion. Since there were no complications following ESWL treatment, we can conclude that, in short term, ESWL is an effective and safe treatment modality for renal lithiasis in infants. In addition, we recommend double-J stent in infants with stones larger than 13 mm

N,N′,N′′,N′′′-Tetrakis(2-methylphenyl)­oxybis(phospho­nic diamide): a redetermination at 150 K with Mo Kα radiation

The structure of the title compound, C28H32N4O3P2, has been redetermined at 150 K, with much improved precision. The structure and mol­ecular packing of the title compound was previously determined using Cu Kα radiation, with an R value of 0.0933 [Cameron et al. (1978 ▶). Z. Naturforsch. Teil B, 33, 728–730]. The c-axis length in this structure [13.8401 (8) Å] is almost half that reported in the original study. In the title compound, two (C6H4(2-CH3)NH)2P(O) units are bridged via an O atom [P—O—P = 133.31 (11)°]. The P atoms adopt a slightly distorted tetra­hedral coordination geometry. In the crystal, mol­ecules are linked via N—H⋯OP hydrogen bonds into extended chains parallel to the c axis. An intra­molecular N—H⋯O=P hydrogen bond is also found in the mol­ecule

Slender PUF Protocol: A lightweight, robust, and secure authentication by substring matching

We introduce Slender PUF protocol, an efficient and secure method to authenticate the responses generated from a Strong Physical Unclonable Function (PUF). The new method is lightweight, and suitable for energy constrained platforms such as ultra-low power embedded systems for use in identification and authentication applications. The proposed protocol does not follow the classic paradigm of exposing the full PUF responses (or a transformation of the full string of responses) on the communication channel. Instead, random subsets of the responses are revealed and sent for authentication. The response patterns are used for authenticating the prover device with a very high probability.We perform a thorough analysis of the method’s resiliency to various attacks which guides adjustment of our protocol parameters for an efficient and secure implementation. We demonstrate that Slender PUF protocol, if carefully designed, will be resilient against all known machine learning attacks. In addition, it has the great advantage of an inbuilt PUF error tolerance. Thus, Slender PUF protocol is lightweight and does not require costly additional error correction, fuzzy extractors, and hash modules suggested in most previously known PUF-based robust authentication techniques. The low overhead and practicality of the protocol are confirmed by a set of hardware implementation and evaluations