Miracle-dz/UniFAR-FTUs huggingface dataset:
Overview of Sustainable Plant Growth and Differentiation and the Role of Hormones in Controlling Growth and Development of Plants under Various Stresses.

Plant development is different from the animal by many fundamental aspects, immobilized cell, rigid cell wall, and the large central vacuole. The plant growth and cell division activities are restricted to the specific area of the shoot and root called meristems, and the plants have highly totipotency ability of differentiation, dedifferentiation and redifferentiation. Growth is one of the primary and conspicuous characteristics of living organisms. Growth is generally defining the irreversible permanent increase in the size of the organ or even single cell, due to both cell division and expansion. The plants pass through different stages e.g. embryonic, vegetative and reproductive, and in each stage, they produce different organs on the same stem. The height and girth of the plants grow continuously but leaves and flowers have definite dimensional size and fall down periodically. Where and how the structures like shoots, roots, leave and the flower is differentiated from same mother cell (zygote)? All the plant's organs are made of different tissues dermal, vascular and ground tissue and each tissue have precious role in plant survival and development. How the plant organs arrange spatial around the stem axis clock or anti clock wise direction (phyllotaxis)? The plant hormone is one of the important factors which control the plant development; the development is the combine process of growth and differentiation. So the differentiation is the process in which the cell are organized into different shape and functions according to the genetically and environmental signal. In this chapter, we will study both the intrinsic and extrinsic factors which govern the growth and differentiation. Briefly study some basic genetic modification occur in the cell, cell signaling during growth and differentiation. We need to know generally about the three basic structures of the plant organs root, shoot, and leaves.
Hydrogen sulfide perturbs mitochondrial bioenergetics and triggers metabolic reprogramming in colon cells

Unlike most other tissues, the colon epithelium is exposed to high levels of H2S derived from gut microbial metabolism. H2S is a signaling molecule that modulates various physiological effects. It is also a respiratory toxin that inhibits complex IV in the electron transfer chain (ETC). Colon epithelial cells are adapted to high environmental H2S exposure as they harbor an efficient mitochondrial H2S oxidation pathway, which is dedicated to its disposal. Herein, we report that the sulfide oxidation pathway enzymes are apically localized in human colonic crypts at the host–microbiome interface, but that the normal apical-to-crypt gradient is lost in colorectal cancer epithelium. We found that sulfide quinone oxidoreductase (SQR), which catalyzes the committing step in the mitochondrial sulfide oxidation pathway and couples to complex III, is a critical respiratory shield against H2S poisoning. H2S at concentrations ≤20 μm stimulated the oxygen consumption rate in colon epithelial cells, but, when SQR expression was ablated, H2S concentrations as low as 5 μm poisoned cells. Mitochondrial H2S oxidation altered cellular bioenergetics, inducing a reductive shift in the NAD+/NADH redox couple. The consequent electron acceptor insufficiency caused uridine and aspartate deficiency and enhanced glutamine-dependent reductive carboxylation. The metabolomic signature of this H2S-induced stress response mapped, in part, to redox-sensitive nodes in central carbon metabolism. Colorectal cancer tissues and cell lines appeared to counter the growth-restricting effects of H2S by overexpressing sulfide oxidation pathway enzymes. Our findings reveal an alternative mechanism for H2S signaling, arising from alterations in mitochondrial bioenergetics that drive metabolic reprogramming
The Evolving Modern Management of Brain Metastasis

The incidence of brain metastases is increasing as cancer therapies improve and patients live longer, providing new challenges to the multidisciplinary teams that care for these patients. Brain metastatic cancer cells possess unique characteristics that allow them to penetrate the blood–brain barrier, colonize the brain parenchyma, and persist in the intracranial environment. In addition, brain metastases subvert the innate and adaptive immune system, permitting evasion of the antitumor immune response. Better understanding of the above mechanisms will allow for development and delivery of more effective therapies for brain metastases. In this review, we outline the molecular mechanisms underlying development, survival, and immunosuppression of brain metastases. We also discuss current and emerging treatment strategies, including surgery, radiation, disease-specific and mutation-targeted systemic therapy, and immunotherapy.
Tamoxifen Metabolism and Breast Cancer Recurrence: A Question Unanswered by CYPTAM.

In a recent article in Journal of Clinical Oncology, Sanchez-Spitman et al report on their prospective clinical study to assess the impact of CYP2D6 genotype and endoxifen plasma concentrations on relapsefree survival (RFS). They did not observe an association between CYP2D6 genotype or endoxifen concentrations with RFS in women receiving an admixture of chemotherapy, trastuzumab, tamoxifen, and aromatase inhibitors (AIs), and they concluded that neither CYP2D6 genotyping nor endoxifen metabolite monitoring are justified for guiding tamoxifen treatment. However, we will articulate several major issues with their study that provide the basis for serious concern regarding their conclusions.
Preventive Tutoring in Programming: A Tutoring System for Novice Program Design

To address novice deficiencies in strategic programming knowledge, this thesis proposal describes Coached Program Planning (CPP), a tutorial model for structured program design, and subsequently proposes an intelligent tutoring system embodying that model. The aim is to support novices in the transition from an intuitive understanding of the target task to a deeper, more algorithmic view. This is accomplished by eliciting observations and design decisions from the student before they attempt an implementation. In a human-to-human, computer-mediated study, CPP was found to positively impact novice programming behaviors. In addition, the resulting corpus reveals that that novices are generally able to describe what needs to be done (goals) and identify aspects of how to achieve those goals (schemas). Not surprisingly, the language they use shows heavy dependence on the problem statement and on their largely syntactic repertoire of programming knowledge. Various tutoring tactics are used to improve flawed student answers, including simple moves like pumping, and more elaborate, multi-turn tactics like the use of interactive concrete examples. I have built a prototype intelligent tutoring system ProPl (“pro-PELL”) using knowledge construction dialogues (KCDs) as the primary knowledge source. I am proposing to extend this base system by implementing methods that attempt KCD recovery. That is, for utterances not successfully mapped to expected answers, the revised system will execute dialogue moves that attempt to re-establish the line of reasoning modeled in the KCD. The motivation for this approach is the need to accommodate novices’ range of linguistic expression of program design concepts. I intend to evaluate the system in terms of problem decomposition skills and general programming competence. I hope to make contributions by enhancing the general understanding of novice programmers and by providing methods for more robust tutorial dialogue.
Technology class format versus traditional class format in undergraduate algebra

The purpose of this study was to compare the effectiveness of two teaching strategies, namely, technology‐assisted and traditional methods of teaching in undergraduate algebra classes. Fifty‐eight second‐year mathematics education undergraduate students in an algebra class were randomly divided in equal numbers into two groups. One group was taught in a traditional classroom setting and the other group was taught in a technology‐rich environment. In order to be in a good position to judge the quality and extent of student learning, a pre‐teaching test was administered to find out how much algebra the students knew before the start of lectures. An identical test was re‐administered to both classes as post‐teaching tests seven weeks later. The effects of teaching were examined between the classes, as well as within each class. Investigations on whether students’ gender was a determinant of their scores in the pre–post teaching tests were carried out, and independent t‐tests were conducted to determine this aspect. There was evidence to suggest that the incorporation of technology in teaching algebra can improve students’ achievement in algebra.
Quantum cluster approach to the topological invariants in correlated Chern insulators

We detect the topological properties of Chern insulators with strong Coulomb interactions by use of cluster perturbation theory and variational cluster approach. The common scheme in previous studies only involves the calculation of the interacting bulk Chern number within the natural-unit cell by means of the so-called topological Hamiltonian. With close investigations on a prototype model, the half-filled Haldane Hubbard model, which is subject to both periodic and open boundary conditions, we uncover the unexpected failure of this scheme due to the explicit breaking of the translation symmetry. Instead, we assert that the faithful interacting bulk Chern number in the framework of quantum cluster approaches can be computed in the enlarged unit cell, which is free of the fault caused by the explicit translation symmetry breaking and consistent with the interacting bulk-edge correspondence.
Exponential asymptotics for the eigenvalues in the broken PT-symmetric region

Stemming from the seminal work of Bender & Boettcher in 1998 (Phys. Rev. Lett. vol. 80 pp. 5243-5246), there has been great interest in the study of PT-symmetric models of quantum mechanics, where the primary focus is with the study of non-Hermitian Hamiltonians that nevertheless produce countably infinite sets of real-valued eigenvalues. One of the fundamental models of such a system is governed by the Hamiltonian $H = \hat{p}^2 + x^2(ix)^{\varepsilon}$. In their work, Bender & Boettcher proposed a WKB methodology for the prediction of the discrete eigenvalues in the so-called unbroken region of $\varepsilon > 0$. However, the authors noted that this methodology fails to predict those 'broken' eigenvalues for $\varepsilon < 0$. Here, we shall explain why the traditional WKB methodology fails, and we shall demonstrate how eigenvalues for all relevant values of $\varepsilon$ can be predicted using techniques in exponential asymptotics. These predictions provide excellent agreement to exact numerical results over nearly the entire range of values. Moreover, such techniques can be extended to a much wider range PT-symmetric problems.
Provable Gradient Variance Guarantees for Black-Box Variational Inference

Recent variational inference methods use stochastic gradient estimators whose variance is not well understood. Theoretical guarantees for these estimators are important to understand when these methods will or will not work. This paper gives bounds for the common "reparameterization" estimators when the target is smooth and the variational family is a location-scale distribution. These bounds are unimprovable and thus provide the best possible guarantees under the stated assumptions.
Silicate features in the circumstellar envelopes of the Class I binary driving source of HH 250

Context. The silicate feature near 10 μm is one of the main tools available to study the mineralogy of circumstellar disks and envelopes, providing information on the thermal processing, growth, location, and circulation of dust grains. Aims. We investigate the silicate feature of the two Class I components of HH 250-IRS, a resolved binary system with a separation of 0″53 driving a Herbig-Haro flow. Each component has its own circumstellar envelope, and the system is surrounded by a circumbinary disk. Methods. We carried out low resolution spectroscopy in the 8–13 μm range using VISIR, the thermal infrared imager and spectrograph at ESO’s Very Large Telescope. Results. The silicate features of both sources are clearly different. The northwest (NW) component has a broad, smooth absorption profile lacking structure. We attribute most of it to foreground interstellar dust absorption, but estimate that additional absorption by amorphous silicates takes place in the circumstellar envelope of the young stellar object. The southeast (SE) component shows the silicate feature in emission, with structure longward of 9.5 μm indicating the presence of crystalline dust in the dominant form of forsterite. The apparent lack of an absorption feature caused by foreground dust is probably due to the filling of the band with emission by amorphous silicates in the envelope of the object. Conclusions. Despite their virtually certain coevality, the differences in the components of the HH 250-IRS binary are most likely due to markedly different circumstellar environments. The NW component displays an unevolved envelope, whereas dust growth and crystallization has taken place in the SE component. The weak or absent signatures of enstatite in the latter are fairly unusual among envelopes with crystalline dust, and we tentatively relate it to a possible wide gap or an inner truncation of the disk already hinted at in previous observations by a drop in the L′-band flux, which might indicate that the SE component could actually be a very close binary. We speculate that the clear differences between the silicate feature spectra of both components of HH 250-IRS may be due either to disk evolution sped up by multiplicity, or by accretion variability leading to episodes of crystal formation. Different inclinations with respect to the line of sight may play a role as well, although it is very unlikely that they are the sole element for the differences between both objects.
Noise reduction algorithms for digital hearing aids

Speech intelligibility is reduced for most listeners in the presence of background noise. Hearing-impaired listeners face special difficulties in noise which are often exacerbated by conventional analog hearing aids. Noise reduction techniques based on waveform representations, while improving signal-to-noise ratio (SNR), produce only modest intelligibility gains. A new approach based on time-varying parametric modeling is proposed.
Cascaded Cross-Module Residual Learning towards Lightweight End-to-End Speech Coding

Speech codecs learn compact representations of speech signals to facilitate data transmission. Many recent deep neural network (DNN) based end-to-end speech codecs achieve low bitrates and high perceptual quality at the cost of model complexity. We propose a cross-module residual learning (CMRL) pipeline as a module carrier with each module reconstructing the residual from its preceding modules. CMRL differs from other DNN-based speech codecs, in that rather than modeling speech compression problem in a single large neural network, it optimizes a series of less-complicated modules in a two-phase training scheme. The proposed method shows better objective performance than AMR-WB and the state-of-the-art DNN-based speech codec with a similar network architecture. As an end-to-end model, it takes raw PCM signals as an input, but is also compatible with linear predictive coding (LPC), showing better subjective quality at high bitrates than AMR-WB and OPUS. The gain is achieved by using only 0.9 million trainable parameters, a significantly less complex architecture than the other DNN-based codecs in the literature.
The Effect of Spatial Correlation on the Performance of Uplink and Downlink Single-Carrier Massive MIMO Systems

We present the analysis of a single-carrier massive MIMO system for the frequency selective Gaussian multi-user channel, in both uplink and downlink directions. We develop expressions for the achievable sum rate when there is spatial correlation among antennas at the base station. It is known that the channel matched filter precoder (CMFP) performs the best in a spatially uncorrelated downlink channel. However, we show that, in a spatially correlated downlink channel with two different correlation patterns and at high long-term average power, two other precoders have better performance. For the uplink channel, part of the equivalent noise in the channel goes away, and implementing two conventional equalizers leads to a better performance compared to the channel matched filter equalizer (CMFE). These results are verified for uniform linear and uniform planar arrays. In the latter, due to more correlation, the performance drop with a spatially correlated channel is larger, but the performance gain against channel matched filter precoder or equalizer is also bigger. In highly correlated cases, the performance can be a significant multiple of that of the channel matched filter precoder or equalizers. }
VizADS-B: Analyzing Sequences of ADS-B Images Using Explainable Convolutional LSTM Encoder-Decoder to Detect Cyber Attacks

The purpose of the automatic dependent surveillance broadcast (ADS-B) technology is to serve as a replacement for the current radar-based, air traffic control systems. Despite the considerable time and resources devoted to designing and developing the system, the ADS-B is well known for its lack of security mechanisms. Attempts to address these security vulnerabilities have been made in previous studies by modifying the protocol's current architecture or by using additional hardware components. These solutions, however, are considered impractical because of 1) the complex regulatory process involving avionic systems, 2) the high costs of using hardware components, and 3) the fact that the ADS-B system itself is already deployed in most aircraft and ground stations around the world. In this paper, we propose VizADS-B, an alternative software-based security solution for detecting anomalous ADS-B messages, which does not require any alteration of the current ADS-B architecture or the addition of sensors. According to the proposed method, the information obtained from all aircraft within a specific geographical area is aggregated and represented as a stream of images. Then, a convolutional LSTM encoder-decoder model is used for analyzing and detecting anomalies in the sequences of images. In addition, we propose an explainability technique, designed specifically for convolutional LSTM encoder-decoder models, which is used for providing operative information to the pilot as a visual indicator of a detected anomaly, thus allowing the pilot to make relevant decisions. We evaluated our proposed method on five datasets by injecting and subsequently identifying five different attacks. Our experiments demonstrate that most of the attacks can be detected based on spatio-temporal anomaly detection approach.
The XMM-Newton wide field survey in the COSMOS field: Clustering dependence of X-ray selected AGN on host galaxy properties

Aims. We study the spatial clustering of 632 (1130) XMM-COSMOS active galactic nuclei (AGNs) with known spectroscopic or photometric redshifts in the range z = [0.1–2.5] in order to measure the AGN bias and estimate the typical mass of the hosting dark matter (DM) halo as a function of AGN host galaxy properties. Methods. We created AGN subsamples in terms of stellar mass, M*, and specific black hole accretion rate, LX/M*, to study how AGN environment depends on these quantities. Further, we derived the M*−Mhalo relation for our sample of XMM-COSMOS AGNs and compared it to results in literature for normal non-active galaxies. We measured the projected two-point correlation function wp(rp) using both the classic and the generalized clustering estimator, based on photometric redshifts, as probability distribution functions in addition to any available spectroscopic redshifts. We measured the large-scale (rp ≳ 1 h−1 Mpc) linear bias b by comparing the clustering signal to that expected of the underlying DM distribution. The bias was then related to the typical mass of the hosting halo Mhalo of our AGN subsamples. Since M* and LX/M* are correlated, we matched the distribution in terms of one quantity and we split the distribution in the other. Results. For the full spectroscopic AGN sample, we measured a typical DM halo mass of log (Mhalo/h−1 M⊙) = 12.79−0.43+0.26, similar to galaxy group environments and in line with previous studies for moderate-luminosity X-ray selected AGN. We find no significant dependence on specific accretion rate LX/M*, with log (Mhalo/h−1 M⊙) = 13.06−0.38+0.23 and log (Mhalo/h−1 M⊙) = 12.97−1.26+0.39 for low and high LX/M* subsamples, respectively. We also find no difference in the hosting halos in terms of M* with log (Mhalo/h−1 M⊙) = 12.93−0.62+0.31 (low) and log (Mhalo/h−1 M⊙) = 12.90−0.62+0.30 (high). By comparing the M*−Mhalo relation derived for XMM-COSMOS AGN subsamples with what is expected for normal non-active galaxies by abundance matching and clustering results, we find that the typical DM halo mass of our high M* AGN subsample is similar to that of non-active galaxies. However, AGNs in our low M* subsample are found in more massive halos than non-active galaxies. By excluding AGNs in galaxy groups from the clustering analysis, we find evidence that the result for low M* may be due to larger fraction of AGNs as satellites in massive halos.
Disentangling feature and lazy learning in deep neural networks: an empirical study

Two distinct limits for deep learning as the net width $h\to\infty$ have been proposed, depending on how the weights of the last layer scale with $h$. In the "lazy-learning" regime, the dynamics becomes linear in the weights and is described by a Neural Tangent Kernel $\Theta$. By contrast, in the "feature-learning" regime, the dynamics can be expressed in terms of the density distribution of the weights. Understanding which regime describes accurately practical architectures and which one leads to better performance remains a challenge. We answer these questions and produce new characterizations of these regimes for the MNIST data set, by considering deep nets $f$ whose last layer of weights scales as $\frac{\alpha}{\sqrt{h}}$ at initialization, where $\alpha$ is a parameter we vary. We performed systematic experiments on two setups (A) fully-connected Softplus momentum full batch and (B) convolutional ReLU momentum stochastic. We find that (1) $\alpha^*=\frac{1}{\sqrt{h}}$ separates the two regimes. (2) for (A) and (B) feature learning outperforms lazy learning, a difference in performance that decreases with $h$ and becomes hardly detectable asymptotically for (A) but is very significant for (B). (3) In both regimes, the fluctuations $\delta f$ induced by initial conditions on the learned function follow $\delta f\sim1/\sqrt{h}$, leading to a performance that increases with $h$. This improvement can be instead obtained at intermediate $h$ values by ensemble averaging different networks. (4) In the feature regime there exists a time scale $t_1\sim\alpha\sqrt{h}$, such that for $t\ll t_1$ the dynamics is linear. At $t\sim t_1$, the output has grown by a magnitude $\sqrt{h}$ and the changes of the tangent kernel $\|\Delta\Theta\|$ become significant. Ultimately, it follows $\|\Delta\Theta\|\sim(\sqrt{h}\alpha)^{-a}$ for ReLU and Softplus activation, with $a<2$ & $a\to2$ when depth grows.