Abiotic stress is one of the severe stresses of environment that lowers the growth and yield of any crop even on irrigated land throughout the world. A major phytohormone abscisic acid (ABA) plays an essential part in acting toward varied range of stresses like heavy metal stress, drought, thermal or heat stress, high level of salinity, low temperature, and radiation stress. Its role is also elaborated in various developmental processes including seed germination, seed dormancy, and closure of stomata. ABA acts by modifying the expression level of gene and subsequent analysis of cis-and trans-acting regulatory elements of responsive promoters. It also interacts with the signaling molecules of processes involved in stress response and development of seeds. On the whole, the stress to a plant can be susceptible or tolerant by taking into account the coordinated activities of various stress-responsive genes. Numbers of transcription factor are involved in regulating the expression of ABA responsive genes by acting together with their respective cis-acting elements. Hence, for improvement in stresstolerance capacity of plants, it is necessary to understand the mechanism behind it. On this ground, this article enlightens the importance and role of ABA signaling with regard to various stresses as well as regulation of ABA biosynthetic pathway along with the transcription factors for stress tolerance.

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Abstract-As communication systems scale up in speed and bandwidth, the cost and power consumption of high-precision (e.g., 8-12 bits) analog-to-digital conversion (ADC) becomes the limiting factor in modern transceiver architectures based on digital signal processing. In this work, we explore the impact of lowering the precision of the ADC on the performance of the communication link. Specifically, we evaluate the communication limits imposed by low-precision ADC (e.g., 1-3 bits) for transmission over the real discrete-time Additive White Gaussian Noise (AWGN) channel, under an average power constraint on the input. For an ADC with K quantization bins (i.e., a precision of log 2 K bits), we show that the input distribution need not have any more than K+1 mass points to achieve the channel capacity. For 2-bin (1-bit) symmetric quantization, this result is tightened to show that binary antipodal signaling is optimum for any signal-tonoise ratio (SNR). For multi-bit quantization, a dual formulation of the channel capacity problem is used to obtain tight upper bounds on the capacity. The cutting-plane algorithm is employed to compute the capacity numerically, and the results obtained are used to make the following encouraging observations : (a) up to a moderately high SNR of 20 dB, 2-3 bit quantization results in only 10-20% reduction of spectral efficiency compared to unquantized observations, (b) standard equiprobable pulse amplitude modulated input with quantizer thresholds set to implement maximum likelihood hard decisions is asymptotically optimum at high SNR, and works well at low to moderate SNRs as well.

Motivation The recent discovery of numerous non-coding RNAs (long non-coding RNAs, in particular) has transformed our perception about the roles of RNAs in living organisms. Our ability to understand them, however, is hampered by our inability to solve their secondary and tertiary structures in high resolution efficiently by existing experimental techniques. Computational prediction of RNA secondary structure, on the other hand, has received much-needed improvement, recently, through deep learning of a large approximate data, followed by transfer learning with gold-standard base-pairing structures from high-resolution 3-D structures. Here, we expand this single-sequence-based learning to the use of evolutionary profiles and mutational coupling. Results The new method allows large improvement not only in canonical base-pairs (RNA secondary structures) but more so in base-pairing associated with tertiary interactions such as pseudoknots, noncanonical and lone base-pairs. In particular, it is highly accurate for those RNAs of more than 1000 homologous sequences by achieving >0.8 F1-score (harmonic mean of sensitivity and precision) for 14/16 RNAs tested. The method can also significantly improve base-pairing prediction by incorporating artificial but functional homologous sequences generated from deep mutational scanning without any modification. The fully automatic method (publicly available as server and standalone software) should provide the scientific community a new powerful tool to capture not only the secondary structure but also tertiary base-pairing information for building three-dimensional models. It also highlights the future of accurately solving the base-pairing structure by using a large number of natural and/or artificial homologous sequences. Availability Standalone-version of SPOT-RNA2 is available at https://github.com/jaswindersingh2/SPOT-RNA2. Direct prediction can also be made at https://sparks-lab.org/server/spot-rna2/. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above.

Abstract-The economies of scale in modern communication systems are enabled by architectures that take advantage of Moore's law to implement most transceiver functionalities in digital signal processing (DSP). The bottleneck in scaling such "mostly digital" architectures to multi-Gigabit rates becomes the analog-to-digital converter (ADC): high-speed, high-precision ADCs are either not available, or are too costly and powerhungry. In this paper, we report on recent results on two approaches towards addressing this bottleneck. The first is to simply use drastically low-precision (1-4 bit) ADCs than current practice. This could be suitable for applications that require limited dynamic range (e.g., line-of-sight communication using small constellations), but there are fundamental and algorithmic questions as to whether all the functions of a communication receiver can be realized with such a significant nonlinearity early in the processing. The second is to use a time-interleaved ADC, where a large number of low-speed, high-precision ADCs are employed in parallel to realize a high-speed, high-precision ADC. This is more generally applicable to applications requiring large dynamic range (e.g., large constellations and/or dispersive channels), but the important question is how to effectively address the mismatch between the component ADCs, which leads to a performance floor if left uncompensated.