Miracle-dz/UniFAR-FTUs huggingface dataset:
The combined actions of the copper‐responsive repressor CsoR and copper‐metallochaperone CopZ modulate CopA‐mediated copper efflux in the intracellular pathogen Listeria monocytogenes

We have characterized the csoR‐copA‐copZ copper resistance operon of the important human intracellular pathogen Listeria monocytogenes. Transcription of the operon is specifically induced by copper, and mutants lacking the P1‐type ATPase CopA have reduced copper tolerance and over‐accumulate copper relative to wild type. The copper‐responsive repressor CsoR autoregulates transcription by binding to a single 32 bp site spanning the −10 and −35 elements of the promoter. Copper co‐ordination by CsoR derepresses transcription of the operon and alters CsoR:DNA complex assembly as determined by DNase I footprinting and electrophoretic mobility shift assays, with some DNA‐binding capacity being retained in the presence of 2 mole equivalents of copper. Analysis of the CsoR copper sensory site demonstrated that substitution of Cys42 with Ala generated a CsoR variant that was unresponsive to copper. Importantly, in the absence of CopZ, copper responsiveness of csoR‐copA‐copZ expression is substantially increased, implying that CopZ reduces the access of CsoR to copper. Furthermore, CopZ is shown to confer copper resistance in mutants lacking copper‐inducible csoR‐copA‐copZ expression, thus providing protection from the deleterious effects of copper within the cytoplasm.
An Adaptive Prognostic Approach Incorporating Inspection Influence for Deteriorating Systems

Degradation data obtained through inspections have been widely used to estimate the remaining useful life (RUL) of deteriorating systems. At the same time, such inspections may introduce external stress or release interior stress on the degradation processes of systems. However, current studies have paid little attention to the influence of such inspections during degradation modeling and RUL estimation. In this paper, we incorporate the inspection influence into Wiener-process-based degradation modeling and develop an approach to estimate the RUL of deteriorating systems. In the proposed method, the lifetime and RUL distribution with the consideration of the inspection influence on degrading systems are derived under the concept of first hitting time. To achieve an adaptive estimation of the RUL when new observations arrive, we constructed a state-space model by augmenting the drift coefficient and the inspection influence as state variables and treating the degradation increments as the observation variables. To do so, a Kalman filter is employed to update the state estimation, and then, an analytical result of the estimated RUL distribution for periodic-inspected systems is achieved. To implement the proposed prognostic method, we apply the expectation maximization algorithm to estimate unknown parameters in the constructed state-space model based on the historical observations of inspections. Finally, the proposed approach is illustrated by a numerical example and demonstrated by a case study using the mechanical gyroscopes.
Deep Learning-Based Super-Resolution Applied to Dental Computed Tomography

The resolution of dental computed tomography (CT) images is limited by detector geometry, sensitivity, patient movement, the reconstruction technique and the need to minimize radiation dose. Recently, the use of convolutional neural network (CNN) architectures has shown promise as a resolution enhancement method. In the current work, two CNN architectures—a subpixel network and the so called U-net—have been considered for the resolution enhancement of 2-D cone-beam CT image slices of ex vivo teeth. To do so, a training set of 5680 cross-sectional slices of 13 teeth and a test set of 1824 slices of 4 structurally different teeth were used. Two existing reconstruction-based super-resolution methods using $\boldsymbol {\ell _{2}}$ -norm and total variation regularization were used for comparison. The results were evaluated with different metrics (peak signal-to-noise ratio, structure similarity index, and other objective measures estimating human perception) and subsequent image-segmentation-based analysis. In the evaluation, micro-CT images were used as ground truth. The results suggest the superiority of the proposed CNN-based approaches over reconstruction-based methods in the case of dental CT images, allowing better detection of medically salient features, such as the size, shape, or curvature of the root canal.
Extracting Significant Mobile Phone Interaction Patterns Based on Community Structures

Mobile phones have emerged as an essential part of people’s lives. The data produced from them can be utilized to derive the spatio-temporal information of their users’ whereabouts. We can obtain a rich data set of human activities, interactions, social relationships, and mobility. Hence, it has been possible to explore these information sources with applications ranging from disaster management to disease epidemiology. In this paper, we have focused on the use of call detail records to explore and interpret patterns embedded in interaction flows of people through their mobile phone calls. To do so, we consider the geographical context of subscribers/celltowers to discover structures of spatio-temporal interactions and communities’ patterns in Macau. We have explored the inter and intra-polygon interaction flows. The results suggest that subscribers tend to communicate within a spatial-proximity community. In order to delineate relatively contiguous objects with similar attribute values, we have implemented an efficient hierarchical clustering approach. By identifying key objects and their close associates and exploring their communication patterns, we can detect shared interests and dominant interactions that influence societal patterns. Such insight is useful for resource optimization in network planning, content distribution, and urban planning.
A larger transcript is required for the synthesis of the smaller isoform of ferredoxin:NADP oxidoreductase

Ferredoxin:NADP oxidoreductases (FNRs) constitute a family of flavoenzymes that catalyse the exchange of electrons between ferredoxin and NADP(H). In cyanobacteria FNR provides NADPH for photoautotrophic metabolism, but the enzyme is also capable of oxidizing NADPH providing reduced ferredoxin. In the cyanobacterium Synechocystis sp. strain PCC6803, the unique petH gene has two translation products depending on growth conditions. As a consequence two isoforms of the FNR accumulate – FNRL and FNRS. In the present work, analysis of petH expression reveals that different transcriptional start points (tsp) are responsible for this differential translation initiation. Under standard conditions (where FNRL accumulates), two tsps were found at −52 and −34 relative to the first translation start site. Under nitrogen‐starvation conditions (where FNRS accumulates) a tsp was mapped at −126 relative to the first translation start site. Therefore, the transcript responsible for FNRS translation is longer than that producing FNRL. In addition, expression of the short or long transcript in E. coli resulted in the accumulation of FNRL or FNRS respectively. This result demonstrates that translation can initiate at two different sites, 336‐bases apart (ATG‐1 to ATG‐113), depending only on the 5′UTR structure.
A Novel Point Cloud Compression Algorithm Based on Clustering

Due to the enormous volume of point cloud data, transmitting and storing the data requires large bandwidth and storage space. It could be a critical bottleneck, especially in tasks such as autonomous driving. In this letter, we propose a novel point cloud compression algorithm based on clustering. The proposed scheme starts with a range image-based segmentation step, which segments the three-dimensional (3-D) range data into ground and main objects. Then, it introduces a novel prediction method according to the segmented regions’ shape. This prediction method is inspired by the depth modeling modes used in 3-D high-efficiency video coding for depth map coding. Finally, the few prediction residual is efficiently compressed with several lossless or lossy data compression techniques. Experimental results show that the proposed algorithm can largely eliminate the spatial redundant information of the point cloud data. The lossless compression scheme reaches a compression ratio of nearly 5%, which means that the point cloud is compressed to 5% of its original size without any distance distortion. Compared with other methods, the proposed compression algorithm also shows better performance.
Deep Neural Networks for Channel Estimation in Underwater Acoustic OFDM Systems

Orthogonal frequency division multiplexing (OFDM) provides a promising modulation technique for underwater acoustic (UWA) communication systems. It is indispensable to obtain channel state information for channel estimation to handle the various channel distortions and interferences. However, the conventional channel estimation methods such as least square (LS), minimum mean square error (MMSE) and back propagation neural network (BPNN) cannot be directly applied to UWA-OFDM systems, since complicated multipath channels may cause a serious decline in performance estimation. To address the issue, two types of channel estimators based on deep neural networks (DNNs) are proposed with a novel training strategy in this paper. The proposed DNN models are trained with the received pilot symbols and the correct channel impulse responses in the training process, and then the estimated channel impulse responses are offered by the proposed DNN models in the working process. The experimental results demonstrate that the proposed methods outperform LS, BPNN algorithms and are comparable to the MMSE algorithm in respect to bit error rate and normalized mean square error. Meanwhile, there is no requirement of prior statistics information about channel autocorrelation matrix and noise variance for our proposals to estimate channels in UWA-OFDM systems, which is superior to the MMSE algorithm. Our proposed DNN models achieve better performance using 16QAM than 32QAM, 64QAM, furthermore, the specified DNN architectures help improve real-time performance by saving runtime and storage resources for online UWA communications.
Electrical Characterization of BEOL Plasma-Induced Damage in Bulk FinFET Technology

Due to the small size of the transistors affected by plasma-induced damage (PID), the electrical characterization of this effect is challenging. In this paper, dc characteristics, breakdown measurements and charge pumping results are combined to extract information about the impact of PID occurring during BEOL processing in an advanced bulk FinFET technology. The effect of PID is visible on the threshold voltage and the transconductance, but not on the gate current and the subthreshold swing, suggesting that fixed charges are responsible for this degradation rather than trap creation. This is confirmed with time-dependent dielectric breakdown measurements. Nonetheless, charge pumping measurements demonstrate that PID increases the density of traps by a factor 1.78, even though the absolute density of traps remain too low to affect significantly the dc characteristics of the transistors.
Optical Metasurface Based on Subwavelength Nanoplasmonic Metamaterial-Lined Apertures

We demonstrate the design and nanofabrication of a new class of metasurface based on arrays of densely packed subwavelength apertures made to resonate through the inclusion of nanostructured plasmonic epsilon-negative and near-zero metamaterials. These metasurfaces exhibit high-contrast fano-like resonance-antiresonance transmission profiles with polarization insensitivity and extreme versatility in design. The proposed metasurfaces push the boundaries of current top-down nanofabrication technologies with sub-10-nm features, and are ideal for a wide variety of innovative applications throughout the electromagnetic spectrum, including subwavelength imaging for material characterization, planar phase-gradient mirrors for cavity quantum electrodynamics, and fully optical switches for integrated optical systems. We report a metasurface design operating in the near-infrared region (150–300 THz) with a typical unit-cell electrical size of one fifth of the free-space wavelength and a remarkable resonance-antiresonance contrast of up to 35 dB.
Analysis, Design, and Implementation of DC–DC IBBC-DAHB Converter With Voltage Matching to Improve Efficiency

The interleaved bidirectional buck–boost converter dual active half-bridge (IBBC-DAHB) dc–dc converter consists of two converters IBBC and DAHB and a coupled inductor. The IBBC-DAHB converter has significant features such as low ripple current at the low-voltage side, low current stress on the input inductors, voltage stress across the switches less than the high-voltage side, high voltage gain, and zero-voltage soft switching. The converter offers high efficiency using phase-shift modulation when it operates at a nominal design operating point. However, the efficiency decreases due to high circulating current, high current stress, and limited soft-switching range when the source and load voltages deviate from their nominal values. This paper employs both duty cycle and phase-shift modulation methods to improve the above mentioned shortcomings using voltage matching for the DAHB converter. The proposed converter is analyzed and a 500-W experimental prototype with 40–60 V input voltage and 400 V output voltage under 100 kHz switching frequency is implemented to verify the theoretical results. The maximum efficiency of 97.15% is achieved at 60 V and 400 W. A flat efficiency characteristic for the converter is derived by the proposed control method, where the efficiency at 20% of nominal load is 91% and much higher than the phase-shift control, which is 70.8%.
Locus: A System and a Language for Program Optimization

We discuss the design and the implementation of Locus, a system and a language to orchestrate the optimization of applications. The increasing complexity of machines and the large space of program variants, produced by the many transformations available, conspire to make compilers deliver unsatisfactory performance. As a result, optimization experts must intervene to manually explore the space of program variants seeking the best version for each target machine. This intervention is unproductive, and maintaining and managing sequences of transformations as new architectures are adopted and new application features are incorporated is challenging.Locus allows collections of program transformation sequences to be specified separately from the application code. The language is able to represent in a clear notation complex collections of transformations that are applied to code regions selected by the programmer. The system integrates multiple optimization modules as well as search modules that facilitate the efficient traversal of the space of program variants. Locus is intended to help experts in the optimization process, specially for complex, long-lived applications that are to be executed on different environments. Four examples are presented to illustrate the power and simplicity of the language. Although not the primary focus of this paper, the examples also show that exploring the space of variants typically leads to better performing codes than those produced by conventional compiler optimizations that are based on heuristics.
Tunable Goos–Hänchen Shift and Polarization Beam Splitting Through a Cavity Containing Double Ladder Energy Level System

The present paper theoretically demonstrates tunable Goos–Hänchen (GH) shift and beam splitting of the polarized light reflected from the cavity containing the double ladder energy level system. Simultaneously opposite GH shifts for left-circularly polarized (LCP) and right-circularly polarized (RCP) beams can be achieved under asymmetric field conditions. By adjusting the intensity (Rabi frequency) of probe or drive field, the GH shifts of LCP and RCP probe beams are manipulated at the same time. We also discuss the effects of probe field frequency detuning on GH shifts and identify the parameters region to obtain a large separate distance (∼320 μm) between LCP and RCP probe beams.
A Simple Method on Generating Synthetic Data for Training Real-time Object Detection Networks

Environment recognition has been an important topic ever since the emergence of augmented reality (AR). For better experience in AR applications, environment recognition should be provided fast in real-time, where real-time object detection technologies could fulfill this requirement. However, training object detectors for AR specific scenarios are often troublesome. The real-time nature of AR produces visual degradations such as motion blur or occlusion by interaction, which make detectors trained with plain data difficult to detect objects exposed in such complex situations. Also, since gathering and labeling training data from scratch is a heavy burden, we need to resort to synthesized training data but previous synthetic data generation frameworks do not consider the aforementioned issue. Therefore, in this paper, we propose a new synthetic data generation framework which includes visual variations such as motion blur and occlusion occurred by distractors. By this simple modification, we show that including such variated data to the training dataset could dramatically improve realtime performance of object detectors by a high margin. Also, we stress that synthesizing training data with no more than three objects per image can achieve competitive performance compared to detectors trained with over four present in a single image. Experimental results both quantitatively and qualitatively supports our statements and shows the superiority of our method.
Implementation methodology for Self-Timed modulators with dual-rail structures based on FPGA

The paper presents the implementation methodology for Self-Timed modulators and their synchronous equivalent. An OQPSK (Offset Quadrature Phase Shift Keying) modulator is used for the ZigBee standard, and is modified with the addition of asynchronous control blocks with dual-rail structures for removing the clock signal. A comparison of synchronous and asynchronous modulators was performed and the implementation was carried out in two different FPGA technologies, where increased performance and reduced on power consumption was observed.
Novel roles of LeuO in transcription regulation of E. coli genome: antagonistic interplay with the universal silencer H‐NS

LeuO, the regulator of leucine biosynthesis operon of Escherichia coli, is involved in the regulation of as yet unspecified genes affecting the stress response and pathogenesis expression. To get insights into the regulatory role(s) of LeuO, Genomic SELEX screening has been performed to identify the whole set of its regulation targets. A total of 140 LeuO‐binding sites were identified on the E. coli genome, of which as many as 133 (95%) were found to contain the binding sites of H‐NS, the universal silencer of stress‐response genes, supporting the concept that LeuO plays an antagonistic role with anti‐silencing activity. Western blot analysis indicated that H‐NS predominates in growing phase; however, after prolonged culture for 1 week, H‐NS decreased instead LeuO increased, supporting the anti‐silencing role of LeuO. In concert with this model, a set of stress‐response genes including cryptic chaperone/usher‐type fimbriae operons are under the control of antagonistic interplay between LeuO and H‐NS. Confocal laser scanning microscopic observation in flow‐chambers showed that the mutants lacking leuO and some fimbriae genes are defective in biofilm formation or form altered biofilm architecture. Taken together we propose that LeuO is a major player in antagonistic interplay against the universal silencer H‐NS.
Threshold Switching and Memory Behaviors of Epitaxially Regrown GaN-on-GaN Vertical p-n Diodes With High Temperature Stability

This letter reports the observation of threshold switching and memory behaviors of epitaxially regrown GaN-on-GaN vertical p-n diodes. This mechanism was ascribed to the conductive path formed by traps in the insulating layer at the regrowth interface after soft breakdown. The device can reliably switch more than 1000 cycles at both room temperature and 300°C with small fluctuation on the set and reset voltage. The set voltage increased with the increasing temperature due to the enhanced thermal detrapping effect that made it harder to form conductive path at high temperatures. Besides, the device showed memory behaviors when the reset voltage was higher than 4.4 V. This letter can serve as an important reference to further developing GaN-based memory devices and integrated circuits.
False Data Injection Attacks With Limited Susceptance Information and New Countermeasures in Smart Grid

In this paper, we consider false data injection (FDI) attacks with limited information of transmission-line susceptances and new countermeasures in smart grids. First, we prove that the adversary could launch FDI attacks to modify the state variable on a bus or superbus only if he/she knows the susceptance of every transmission line that is incident to that bus or superbus. Based on this observation, we provide a new countermeasure against FDI attacks, i.e., to make the susceptances of <inline-formula><tex-math notation="LaTeX">$n-1$</tex-math></inline-formula> interconnected transmission lines that cover all buses unknown to the adversary (e.g., by proactively perturbing transmission-line susceptances through distributed flexible AC transmission system (D-FACTS) devices), where <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the total number of buses. This new countermeasure can work alone or in conjunction with traditional ones to reduce the number of meter measurements/state variables that are to be secured against FDI attacks. The implementation of FDI attacks with limited susceptance information and the effectiveness of new countermeasures are demonstrated by using an illustrative 4-bus power system and the IEEE 9-bus, 14-bus, 30-bus, 118-bus, and 300-bus test power systems.
The C-terminal Region of Human Adipose Triglyceride Lipase Affects Enzyme Activity and Lipid Droplet Binding*

Adipose triglyceride lipase (ATGL) catalyzes the first step in the hydrolysis of triacylglycerol (TG) generating diacylglycerol and free fatty acids. The enzyme requires the activator protein CGI-58 (or ABHD5) for full enzymatic activity. Defective ATGL function causes a recessively inherited disorder named neutral lipid storage disease that is characterized by systemic TG accumulation and myopathy. In this study, we investigated the functional defects associated with mutations in the ATGL gene that cause neutral lipid storage disease. We show that these mutations lead to the expression of either inactive enzymes localizing to lipid droplets (LDs) or enzymatically active lipases with defective LD binding. Additionally, our studies assign important regulatory functions to the C-terminal part of ATGL. Truncated mutant ATGL variants lacking ∼220 amino acids of the C-terminal protein region do not localize to LDs. Interestingly, however, these mutants exhibit substantially increased TG hydrolase activity in vitro (up to 20-fold) compared with the wild-type enzyme, indicating that the C-terminal region suppresses enzyme activity. Protein-protein interaction studies revealed an increased binding of truncated ATGL to CGI-58, suggesting that the C-terminal part interferes with CGI-58 interaction and enzyme activation. Compared with the human enzyme, the C-terminal region of mouse ATGL is much less effective in suppressing enzyme activity, implicating species-dependent differences in enzyme regulation. Together, our results demonstrate that the C-terminal region of ATGL is essential for proper localization of the enzyme and suppresses enzyme activity.