Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparative review on the well-studied GAT1 and the understudied BGT-1 in the brain

γ-aminobutyric acid (GABA) is the primary inhibitory neurotransmitter in the central nervous system (CNS). Its homeostasis is maintained by neuronal and glial GABA transporters (GATs). The four GATs identified in humans are GAT1 (SLC6A1), GAT2 (SLC6A13), GAT3 (SLC6A11), and betaine/GABA transporter-1 BGT-1 (SLC6A12) which are all members of the solute carrier 6 (SLC6) family of sodium-dependent transporters. While GAT1 has been investigated extensively, the other GABA transporters are less studied and their role in CNS is not clearly defined. Altered GABAergic neurotransmission is involved in different diseases, but the importance of the different transporters remained understudied and limits drug targeting. In this review, the well-studied GABA transporter GAT1 is compared with the less-studied BGT-1 with the aim to leverage the knowledge on GAT1 to shed new light on the open questions concerning BGT-1. The most recent knowledge on transporter structure, functions, expression, and localization is discussed along with their specific role as drug targets for neurological and neurodegenerative disorders. We review and discuss data on the binding sites for Na+, Cl−, substrates, and inhibitors by building on the recent cryo-EM structure of GAT1 to highlight specific molecular determinants of transporter functions. The role of the two proteins in GABA homeostasis is investigated by looking at the transport coupling mechanism, as well as structural and kinetic transport models. Furthermore, we review information on selective inhibitors together with the pharmacophore hypothesis of transporter substrates.
Comparative transcriptome analysis provides novel insights into molecular response of salt-tolerant and sensitive polyembryonic mango genotypes to salinity stress at seedling stage

Introduction Increased soil salinity in the recent years has adversely affected the productivity of mango globally. Extending the cultivation of mango in salt affected regions warrants the use of salinity tolerant/resistant rootstocks. However, the lack of sufficient genomic and transcriptomic information impedes comprehensive research at the molecular level. Method We employed RNA sequencing-based transcriptome analysis to gain insight into molecular response to salt stress by using two polyembryonic mango genotypes with contrasting response to salt stress viz., salt tolerant Turpentine and salt susceptible Mylepelian. Results RNA sequencing by Novaseq6000 resulted in a total of 2795088, 17535948, 7813704 and 5544894 clean reads in Mylepelian treated (MT), Mylepelian control (MC), Turpentine treated (TT) and Turpentine control (TC) respectively. In total, 7169 unigenes annotated against all the five public databases, including NR, NT, PFAM, KOG, Swissport, KEGG and GO. Further, maximum number of differentially expressed genes were found between MT and MC (2106) followed by MT vs TT (1158) and TT and TC (587). The differentially expressed genes under different treatment levels included transcription factors (bZIP, NAC, bHLH), genes involved in Calcium-dependent protein kinases (CDPKs), ABA biosynthesis, Photosynthesis etc. Expression of few of these genes was experimentally validated through quantitative real-time PCR (qRT-PCR) and contrasting expression pattern of Auxin Response Factor 2 (ARF2), Late Embryogenesis Abundant (LEA) and CDPK genes were observed between Turpentine and Mylepelian. Discussion The results of this study will be useful in understanding the molecular mechanism underlying salt tolerance in mango which can serve as valuable baseline information to generate new targets in mango breeding for salt tolerance.
Impacts of Tibetan Plateau sensible heat and El Niño–Southern Oscillation on precipitation over South China under the background of the PDO

This study aims to investigate the impacts of the spring sensible heat (SH) over the Tibetan Plateau (TP) and the El Niño–Southern Oscillation (ENSO) in the preceding wintertime on midsummer (July–August) precipitation over South China under the different Pacific decadal oscillation (PDO) phases. More specifically, eight classifications are adopted at the demarcation point around 1996 when the spring SH over the TP and the midsummer precipitation in South China occurred as well as the PDO phase transition, including positive and negative SHs and ENSOs under a positive PDO phase (1979–1996) and a negative PDO phase (1997–2019), respectively, based on the Niño-3 index and the spring SH calculated from 48 stations over the central and eastern parts of the TP. The results show that both the spring SH and the ENSO in preceding wintertime have a significant impact on the midsummer precipitation over South China; that is, when the two factors are in their respective positive (negative) phase, the midsummer precipitation in South China is generally less (more). Importantly, the phase change of background field PDO can significantly enhance the effect of the SH and the ENSO on summer precipitation over South China. Moreover, compared with the preceding wintertime ENSO, the spring SH over the TP contributes more to the midsummer precipitation in South China based on analyses of their independent and synergistic effects. The main mechanism responsible for the anomalous midsummer precipitation over South China are the combined effects of the South Asian high (SAH) and the western Pacific subtropical high (WPSH), which are controlled by the spring SH anomaly over the TP and the ENSO, respectively. Deep understanding of the dominant factors of the midsummer precipitation over South China will help understand the local climate change and reduce the losses caused by drought and flood disasters.
Prediction of drug sensitivity based on multi-omics data using deep learning and similarity network fusion approaches

With the rapid development of multi-omics technologies and accumulation of large-scale bio-datasets, many studies have conducted a more comprehensive understanding of human diseases and drug sensitivity from multiple biomolecules, such as DNA, RNA, proteins and metabolites. Using single omics data is difficult to systematically and comprehensively analyze the complex disease pathology and drug pharmacology. The molecularly targeted therapy-based approaches face some challenges, such as insufficient target gene labeling ability, and no clear targets for non-specific chemotherapeutic drugs. Consequently, the integrated analysis of multi-omics data has become a new direction for scientists to explore the mechanism of disease and drug. However, the available drug sensitivity prediction models based on multi-omics data still have problems such as overfitting, lack of interpretability, difficulties in integrating heterogeneous data, and the prediction accuracy needs to be improved. In this paper, we proposed a novel drug sensitivity prediction (NDSP) model based on deep learning and similarity network fusion approaches, which extracts drug targets using an improved sparse principal component analysis (SPCA) method for each omics data, and construct sample similarity networks based on the sparse feature matrices. Furthermore, the fused similarity networks are put into a deep neural network for training, which greatly reduces the data dimensionality and weakens the risk of overfitting problem. We use three omics of data, RNA sequence, copy number aberration and methylation, and select 35 drugs from Genomics of Drug Sensitivity in Cancer (GDSC) for experiments, including Food and Drug Administration (FDA)-approved targeted drugs, FDA-unapproved targeted drugs and non-specific therapies. Compared with some current deep learning methods, our proposed method can extract highly interpretable biological features to achieve highly accurate sensitivity prediction of targeted and non-specific cancer drugs, which is beneficial for the development of precision oncology beyond targeted therapy.
A novel OSA-related model of intermittent hypoxia in endothelial cells under flow reveals pronounced inflammatory pathway activation

Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder characterized by recurrent episodes of upper airway obstruction and subsequent hypoxia. In patients with OSA, severity and number of these hypoxic events positively correlate with the extent of associated cardiovascular pathology. The molecular mechanisms underlying intermittent hypoxia (IH)-driven cardiovascular disease in OSA, however, remain poorly understood—partly due to the lack of adequate experimental models. Here, we present a novel experimental approach that utilizes primary human endothelial cells cultivated under shear stress. Oxygen partial pressure dynamics were adopted in our in vitro model according to the desaturation-reoxygenation patterns identified in polysomnographic data of severe OSA patients (n = 10, with 892 severe desaturations, SpO2<80%). Using western blot analysis, we detected a robust activation of the two major inflammatory pathways ERK and NF-κB in endothelial cells, whereas no HIF1α and HIF2α protein stabilization was observed. In line with these findings, mRNA and protein expression of the pro-inflammatory adhesion and signaling molecule ICAM-1 and the chemokine CCL2 were significantly increased. Hence, we established a novel in vitro model for deciphering OSA-elicited effects on the vascular endothelium. First data obtained in this model point to the endothelial activation of pro-inflammatory rather than hypoxia-associated pathways in OSA. Future studies in this model might contribute to the development of targeted strategies against OSA-induced, secondary cardiovascular disease.
Gas transfer velocity in the presence of wave breaking

Wave breaking is known to cause air entrainment and enhancement of the near-surface turbulence. Thus, it intensifies the gas exchange across the air–sea interface. Based on the combination of the vertical distribution of the turbulence in the wave-affected layer and the breaking wave-energy dissipation rate in the wave-breaking layer, we proposed a composite model for the gas transfer velocity in the presence of wave breaking. The gas transfer velocity was calculated as a function of the air frictional velocity, wave age, and whitecap coverage. The model was validated by the dependences on winds and wave ages by field and laboratory measurements. The results supported the hypothesis that the large uncertainties in the traditional gas transfer velocities based on wind speed alone at moderate-to-high wind speeds can be ascribed to the neglect of the wind–wave effect, which is mainly attributed to the whitecap coverage as a function of the wind–sea Reynolds number.
Hyperexcitability in the Olfactory Bulb and Impaired Fine Odor Discrimination in the Fmr1 KO Mouse Model of Fragile X Syndrome

Fragile X syndrome (FXS) is the single most common monogenetic cause of autism spectrum disorders (ASDs) in humans. FXS is caused by loss of expression of the fragile X mental retardation protein (FMRP), an mRNA-binding protein encoded on the X chromosome involved in suppressing protein translation. Sensory processing deficits have been a major focus of studies of FXS in both humans and rodent models of FXS, but olfactory deficits remain poorly understood. Here, we conducted experiments in wild-type (WT) and Fmr1 knock-out (KO; Fmr1-/y) mice (males) that lack expression of the gene encoding FMRP to assess olfactory circuit and behavioral abnormalities. In patch-clamp recordings conducted in slices of the olfactory bulb, output mitral cells (MCs) in Fmr1 KO mice displayed greatly enhanced excitation under baseline conditions, as evidenced by a much higher rate of occurrence of spontaneous network-level events known as long-lasting depolarizations (LLDs). The higher probability of spontaneous LLDs (sLLDs), which appeared to be because of a decrease in GABAergic synaptic inhibition in glomeruli leading to more feedforward excitation, caused a reduction in the reliability of stimulation-evoked responses in MCs. In addition, in a go/no-go operant discrimination paradigm, we found that Fmr1 KO mice displayed impaired discrimination of odors in difficult tasks that involved odor mixtures but not altered discrimination of monomolecular odors. We suggest that the Fmr1 KO-induced reduction in MC response reliability is one plausible mechanism for the impaired fine odor discrimination. SIGNIFICANCE STATEMENT Fragile X syndrome (FXS) in humans is associated with a range of debilitating deficits including aberrant sensory processing. One sensory system that has received comparatively little attention in studies in animal models of FXS is olfaction. Here, we report the first comprehensive physiological analysis of circuit defects in the olfactory bulb in the commonly-used Fmr1 knock-out (KO) mouse model of FXS. Our studies indicate that Fmr1 KO alters the local excitation/inhibition balance in the bulb, similar to what Fmr1 KO does in other brain circuits, but through a novel mechanism that involves enhanced feedforward excitation. Furthermore, Fmr1 KO mice display behavioral impairments in fine odor discrimination, an effect that may be explained by changes in neural response reliability.
Uncertainty Quantification Using Multi-Dimensional Hermite Polynomials

The general stochastic problem involves the propagation of input uncertainties through a computation model to arrive at a random output vector. This paper presents the application of the multi-dimensional Hermite polynomials to reduce an unknown random output vector into a significantly simpler unknown vector of numbers. The unknown numbers are evaluated using a collocation method because it has the important practical advantage of allowing existing deterministic numerical codes to be used as “black boxes”. A simple laterally loaded pile example involving two input random variables demonstrated that a third- or fourth-order Hermite expansion is adequate to reproduce probabilities of failure between 10 -3 and 10 -4 . A simple and efficient 2-term recurrence method for obtaining Hermite polynomials of any order in the case of two random dimensions is proposed. To our knowledge, this proposal appears to be original.
Effect of time‐dependent 3‐D electron density gradients on high angle of incidence HF radiowave propagation

One of the challenges for the utilization of HF radiowaves in practical applications is to understand how the signals propagate in time‐ and range‐dependent multipath environments. For typical quiescent ionospheric conditions it is often reasonably straightforward to interpret received HF signals. For disturbed ionospheric conditions, however, such as in the presence of large tilts, irregularities, and medium‐scale traveling ionospheric disturbances (MSTIDs), data interpretation and utilization often becomes challenging. This paper presents a theoretical HF propagation modeling study that exploits the capabilities of a first principles, mesoscale resolution ionosphere code, SAMI3 (Sami3 is Another Model of the Ionosphere) and a new implementation of the 3‐D ray trace equations, MoJo‐15 (Modernized Jones Code) in order to examine the relationship between various HF propagation observables and MSTID characteristics. This paper demonstrates the implications of MSTIDS on high angle of incidence HF propagation during typical low‐latitude, postsunset ionospheric conditions and examines the spatiotemporal evolution of multiple propagation paths that may connect a given source and receiver.
A NONHOMOGENEOUS BACKWARD HEAT PROBLEM: REGULARIZATION AND ERROR ESTIMATES

We consider the problem of finding the initial temperature, from the final temperature, in the nonhomogeneous heat equation ut uxx = f(x,t), (x,t) 2 (0, ) ◊ (0,T), u(0,t) = u(,t ) = 0, (x,t) 2 (0, ) ◊ (0,T). This problem is known as the backward heat problem and is severely ill-posed. Our goal is to present a simple and convenient regularization method, and sharp error estimates for its approximate solutions. We illustrate our results with a numerical example.
A Fast-Transient Response Capacitor-Less FVF-LDO in 22-nm CMOS Technology

This paper presents a capacitor-less low-dropout regulator(LDO) based on flipped voltage follower(FVF) structure to achieve fast-transient response and small voltage spikes with a high power-supply ripple rejection (PSRR) performance. This capacitor-less LDO, implemented in 22 nm CMOS technology, is designed for the Internet-of-Things(IoT) application. With a method of adaptive biasing, this LDO can reduce quiescent by tracking load variations and improve the load transient response by tracing the variation of output voltage. Experimental results show that the maximal overshoot and undershoot with adaptive biasing circuit are about 25.06 mV and 38.73 mV, respectively, at the load current toggling between 100 uA and 10 mA with edge time of 300 ns. At the same time, its PSRR can achieve about -60.7 dB at 100 kHz and -41.2 dB at 1 MHz with quiescent current of 11 uA.
An 8-T Processing-in-Memory SRAM Cell-Based Pixel-Parallel Array Processor for Vision Chips

Vision chip is a high-speed image processing device, featuring a massively-parallel pixel-level processing element (PE) array to boost pixel processing speed. However, the collocated processing unit and fine-grained data memory unit inside each PE impose a huge requirement on memory access bandwidth as well as big area and energy consumption. To overcome this bottleneck, this paper proposes a full custom $\mathbf{8T}$ SRAM-based Processing-in-memory (PIM) architecture to realize pixel-parallel array processor for high-speed energy-efficient vision chips. The proposed PIM architecture is constructed by emending multiplexer-based computing circuits into a dual port 8T SRAM array, so as to form a PIM PE array. Each PIM PE holds a 66-bit 8T SRAM cell block embedding in-memory logic functions, of which 64-bit 8T SRAM cells serving as the PE memory, 2-bit 8T SRAM cells acting as a buffer register in the PE. A full custom physical layout of a 16 $\times \boldsymbol{16}$ prototyping PIM PE array is designed and simulated using a 65 nm CMOS technology. The simulation results demonstrate that our proposed PIM PE architecture can achieve 200 MHz operation at 1.2 V, and reach a high energy efficiency of 3.97 TOPS/W while keeping a compact area of 0.129 $\mathbf{mm}^{2}$.
Reduction of Motion Artifact in PPG signal with CDS-LMS Filter

This paper presents correlated double sampling least mean squares (CDS-LMS) filter to reduce the motion artifacts of the PPG signal. By using red and green PPG signals as the inputs of the LMS filter, the motion artifacts with the same frequency component as PPG signals can be removed without losing information in PPG. Also, CDS is employed before the LMS filter to remove the effect of ambient light offset. With the proposed CDS-LMS filter, the RMS error in heart-beat interval is reduced by 86.49%, 87.54%, and 89.09% compared to a conventional bandpass filter when the PPG signal is measured during squatting, walking, and running. The mean absolute error in heart rate is 2.59 beats per minute while running.
Amicable Orthogonal Designs-Existence

Definition. An orthogonal design in order n and of type (u1, … , us) on the commuting variables x1, . , xs is an n X n matrix, X, with entries from the set ﹛0, ±x1, … , ±xs﹜ such that
Multicommutators and Multiplier Theorems

For all $k,n\in \mathbb N$, we obtain the boundedness of the $n$th dimensional Calder\'on-Coifman-Journ\'e multicommutator \[ (f,a_1,\dots , a_k)\mapsto \mathrm{p.v. } \int_{\mathbb R^n} f(y) \left( \prod_{l=1}^n \dfrac{1}{(y_l-x_l)^{k+1}} \right ) \left[\prod_{j=1}^k\int_{x_1}^{y_1} \cdots \int_{x_n}^{y_n} a_j(u_1, \dots, u_n) \, du_1\! \cdots \! du_n\right] dy \] from $L^{p_0}(\mathbb R^n) \times L^{p_1}(\mathbb R^n) \times \cdots \times L^{p_k}(\mathbb R^n)$ to $L^p(\mathbb R^n) $ in the largest possible open set of indices $(1/p_0, 1/p_1,\dots , 1/p_k )$ with $1/p_0+1/p_1+\dots +1/p_k=1/p$, which is the range $ 1/(k+1)<p<\infty$. Our proof exploits the limited smoothness of the symbol of the multicommutator via a new multilinear multiplier theorem for symbols of restricted smoothness which lie locally in certain Sobolev spaces. Our multiplier approach to this problem is a new contribution in the understanding of Calder\'on's commutator program.
Software-Defined Radio Network Positioning Technology Design. Receiver Processing Procedures

Purpose of software-defined radio (SDR) network positioning technology design is substantiation and experimental validation of new technical solutions for user equipment (UE) location estimation improvement, using infrastructure of existing 4G Long-Term Evolution (LTE) and emerging 5G New Radio (NR) networks, which is highly relevant in Global Navigation Satellite System (GNSS) denied urban environments. Approach for experimental validation is prototyping of eNodeB (eNB) reference signals transmission and UE reception procedures using SDR testbed with Universal Software Radio Peripheral (USRP) hardware and Matlab software. Generation and transmission of reference signals, performed by eNB on the physical layer, is defined by 3GPP technical specification 36.211, and receiver processing procedures, performed by UE, and strongly affecting the accuracy of primary measurements, can be designed as a proprietary solution. Findings of current research include formalization, software realization and results of experimental validation of a sequence of procedures for receiving and processing LTE reference signals in order to collect primary range-difference measurements on the UE side for observed time difference of arrival (OTDOA) positioning with developed SDR testbed. Research and development campaign on the current stage has limitation of SDR testbed validation only in laboratory conditions. Practical implications of developed UE SDR testbed allow to proceed campaign to the field conditions and test proposed technical solution with ambient LTE signals.
Analysis of Power Spectrums in Heart Signals to Detect the Presence of Low-Amplitude High-Frequency Components

Purpose of this study is effective registration of low-amplitude high-frequency components with predictive value - ventricular late potentials (VLPs) - in electrocardiosignals (ECS) by Ist standard lead. 232 signals were preliminary evaluated to detect VLPs by Simson’s method. VLPs were detected in 34 signals. Modification of signal-averaged approach was applicated to analyzed signals. Power spectrums of heartbeat signals’ terminal part were analyzed in this study. Designed algorithm showed 82.75% of correct decisions. Originality: new parameter to detect VLPs by Ist standard lead lead was proposed. Parameter’s threshold value is calculated. Threshold frequencies with maximal percentage of correct decisions were determined. Opportunity to detect VLPs in signals by Ist standard was evaluated: it is possible to detect VLPs using spectral analysis. Practical significance: results will be useful for ECS analysis block in telemedicine systems and onboard communications.