Miracle-dz/UniFAR-FTUs huggingface dataset:
Recurrent Clostridium difficile Infection: Risk Factors, Treatment, and Prevention

The most common cause of antibiotic-associated diarrhea is Clostridium difficile infection (CDI). Recurrent C. difficile infection (rCDI) often occurs after successful treatment of CDI. Due to the increased incidence and the difficulty in treating rCDI, it is becoming an important clinical issue. Identifying risk factors is helpful for early detection, treatment, and prevention of rCDI. Advanced age, use of antibiotics, gastric acid suppression, and infection with a hypervirulent strain are currently regarded as the major risk factors for rCDI. Several treatment modalities, including vancomycin, fidaxomicin, and fecal microbiota transplant (FMT), are suggested for rCDI treatment. However, there is currently no definitive treatment method with sufficient evidence for rCDI. Recent studies have focused on FMT and have shown positive results for rCDI. Prevention of rCDI by measures such as hand washing and isolation of patients is very important. However, these preventive measures are often overlooked in clinical practice. Here, we review the risk factors, treatment, and prevention of rCDI.
Fully Immersible Subcortical Neural Probes With Modular Architecture and a Delta-Sigma ADC Integrated Under Each Electrode for Parallel Readout of 144 Recording Sites

This paper presents a needle-shaped neural probe which integrates an incremental <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula>-ADC under each electrode in an area of <inline-formula> <tex-math notation="LaTeX">$70\times 70\,\,\mu \text{m}^{\mathrm{ 2}}$ </tex-math></inline-formula>. The modular architecture avoids global routing of noise sensitive neural signals and connects with a digital four-wire interface. The re-configurable 5.6/11.5 mm needle enables parallel readout of all 64/144 integrated recording sites and features a constant width and thickness of <inline-formula> <tex-math notation="LaTeX">$70\times 50\,\,\mu \text{m}^{\mathrm{ 2}}$ </tex-math></inline-formula> from top to bottom allowing to be fully implanted for deep brain monitoring applications. Platinum or iridium-oxide electrodes can be deposited in a custom post-CMOS process. The functionality of the recording system is demonstrated in an <italic>in vivo</italic> setup. The noise performance of 6.02 <inline-formula> <tex-math notation="LaTeX">$\mu \text{V}_{\text {rms}}$ </tex-math></inline-formula> for LFP signals (1–300 Hz) and 10.46 <inline-formula> <tex-math notation="LaTeX">$\mu \text{V}_{\text {rms}}$ </tex-math></inline-formula> for AP signals (300 Hz–10 kHz) is obtained at a power consumption of 39.14 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> per recording site.
A Comparison of Deep Learning Architectures for Semantic Mapping of Very High Resolution Images

Semantic mapping of land cover is a key, but challenging, problem in remote sensing. Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have shown outstanding performance in this task. In order to develop refined deep learning pipeline for meeting the rising need for accurate semantic mapping in remote sensing images, this paper study and compare a number of advanced deep learning segmentation architectures, which have obtained state-of-the-art results on computer vision contests like the Pascal VOC. To further analyze and compare the effectiveness of some elaborate layers and underlying structures introduced by these architectures, we evaluate them by re-implementing, train and test them on ISPRS Potsdam dataset. Our results show that a promising performance with overall Fl_score above 87% and mIoU of 79% can be obtained by only using the RGB images, without any post-processing such as conditional random field (CRF) smoothing. At last, we propose several possible approaches to further enhance the deep learning architectures to better deal with high-resolution aerial images. We therefore consider this work to be helpful for the remote sensing research community.
The role of social media usage in enhancing team members' trust and team creativity

The rapid development and increasing popularity of social media has drastically changed online creation. Social media has been widely adopted by organizations over the past decade, which provides opportunities to open up team creativity and innovation processes. This study aims to examine how the social media usage influences team creativity in organizations. Moreover, we examine how trust is affected by social media usage and further affects team creativity. Drawing on the literature of social media and team creativity in organizations, we develop a theoretical framework to explain how social media usage influences team creativity through cognitive trust and affective trust. The result of this study confirms the significant effect of both cognitive trust and affective trust on team creativity and the influence of social media usage on both types of trust. In addition, our findings showed that cognitive trust positively affects affective trust. Based on the results, we also provided theoretical and practical implications.
Linguistic neutrosophic Hamacher aggregation operators and the application in evaluating land reclamation schemes for mines

Land reclamation has become a significant way for the improvement of ecological environment in mining areas. When selecting the optimal land reclamation scheme, LNNs (linguistic neutrosophic numbers) are suitable to describe the complex fuzzy evaluation information through linguistic truth, indeterminacy and falsity membership degrees. Furthermore, the Hamacher aggregation operators are good tools to handle multi-criteria decision making problems. Accordingly, the aim of this paper is to extend Hamacher aggregation operators with LNNs and then build a decision making framework for evaluating land reclamation schemes in mining areas. First, new operational laws of LNNs based on Hamacher t-norm and t-conorm are defined. Then, several linguistic neutrosophic Hamacher aggregation operators, including the linguistic neutrosophic Hamacher weighted mean aggregation operators and linguistic neutrosophic Hamacher hybrid weighted mean aggregation operators are developed. Meanwhile, their desirable properties are proved. Thereafter, a method for decision making with linguistic neutrosophic information based on these operators is proposed to deal with complex decision problems. At last, the validity of this method is confirmed by an illustrative example of evaluating the land reclamation schemes in mining areas. In addition, the impact of the parameter in extended Hamacher aggregation operators is discussed. The merits of the proposed method are also highlighted by comparing with other decision making methods. The results show that the proposed linguistic neutrosophic Hamacher aggregation operators have great flexibility and advantages, and can provide powerful ways for the evaluation of land reclamation schemes.
Leveraging Virtual and Real Person for Unsupervised Person Re-Identification

Person re-identification (re-ID) is a challenging instance retrieval problem, especially when identity annotations are not available for training. Although modern deep re-ID approaches have achieved great improvement, it is still difficult to optimize the deep re-ID model and learn discriminative person representation without annotations in training data. To address this challenge, this study considers the problem of unsupervised person re-ID and introduces a novel approach to solve this problem by leveraging virtual and real data. Our approach includes two components: virtual person generation and training of the deep re-ID model. For virtual person generation, we learn a person generation model and a camera style transfer model using unlabeled real data to generate virtual persons with different poses and camera styles. The virtual data is formed as labeled training data, enabling subsequent training deep re-ID model in supervision. For training of the deep re-ID model, we divide it into three steps: 1) pre-training a coarse re-ID model by using virtual data; 2) collaborative filtering based positive pair mining from the real data; and 3) fine-tuning of the coarse re-ID model by leveraging the mined positive pairs and virtual data. The final re-ID model is achieved by iterating between step 2 and step 3 until convergence. Extensive experiments demonstrate the effectiveness of our method. Experimental results on two large-scale datasets, Market-1501 and DukeMTMC-reID, show the advantages of our method over state-of-the-art approaches in unsupervised person re-ID. Our code is now available online.1
U500 Disposable Patch Insulin Pump: Results and Discussion of a Veterans Affairs Pilot Study

Abstract We present a Veterans Affairs–sponsored pilot study of U500 concentrated insulin administered via disposable patch insulin pump (DPIP) vs twice-daily (BID) injections with an insulin pen in a case series format. We conducted a prospective, single-center, randomized, intent-to-treat pilot study. Ten participants were enrolled with poorly controlled diabetes, defined as hemoglobin A1C >8.0 and severe insulin resistance defined as total daily dose >200 units. Participants were randomized in a 1:1 ratio to either U500 DPIP or U500 BID insulin titration protocols for 14 weeks. A clinical pattern emerged where four participants randomized to the DPIP treatment arm were withdrawn early as the DPIP did not work well for the purpose studied. There was not a statistically significant difference in the rate of hypoglycemia between treatment arms. Based on our clinical experience and results, we argue against the general use of U500 DPIP in clinical practice.
Direct Assimilation of High Resolution ABI Infrared Radiances

Advanced Baseline Imager (ABI) aboard GOES-16 geostationary satellite represents the new generation of GOES imager instruments. It can provide more channels, higher spatial resolution and observation frequency than any previously launched GOES imagers. In this study, impacts of assimilating ABI radiance data at different model horizontal resolutions on quantitative precipitation forecast (QPF) skill over the continental U.S. are examined. The National Centers for Environmental Prediction (NCEP) Gridpoint Statistical interpolation (GSI) analysis system and Advanced Weather Research and Forecast model (WRF-ARW) are employed. After removing cloudy and precipitation-affected brightness temperatures through using an infrared (IR) cloud algorithm and implementing a bias correction, positive impacts are obtained for ABI data assimilation to improve QPFs associated with a typical spring precipitation case. Higher resolution experiments (6 km) improve the QPFs more significantly for heavier rainfall than coarser resolution experiments (15 km).
Correspondence Between Resting-State and Episodic Memory-Task Related Networks in Elderly Subjects

Resting-state fMRI studies demonstrated temporally synchronous fluctuations in brain activity among ensembles of brain regions, suggesting the existence of intrinsic functional networks. A spatial match between some of the resting-state networks and regional brain activation during cognitive tasks has been noted, suggesting that resting-state networks support particular cognitive abilities. However, the spatial match and predictive value of any resting-state network and regional brain activation during episodic memory is only poorly understood. In order to address this research gap, we obtained fMRI acquired both during rest and a face-name association task in 38 healthy elderly subjects. In separate independent component analyses, networks of correlated brain activity during rest or the episodic memory task were identified. For the independent components identified for task-based fMRI, the design matrix of successful encoding or retrieval trials was regressed against the time course of each of the component to identify significantly activated networks. Spatial regression was used to assess the match of resting-state networks against those related to successful memory encoding or retrieval. We found that resting-state networks covering the medial temporal, middle temporal, and frontal areas showed increased activity during successful encoding. Resting-state networks located within posterior brain regions showed increased activity during successful recognition. However, the level of resting-state network connectivity was not predictive of the task-related activity in these networks. These results suggest that a circumscribed number of functional networks detectable during rest become engaged during successful episodic memory. However, higher intrinsic connectivity at rest may not translate into higher network expression during episodic memory.
Biological Potential and Mechanism of Prodigiosin from Serratia marcescens Subsp. lawsoniana in Human Choriocarcinoma and Prostate Cancer Cell Lines

Tripyrrole molecules have received renewed attention due to reports of numerous biological activities, including antifungal, antibacterial, antiprotozoal, antimalarial, immunosuppressive, and anticancer activities. In a screen of bacterial strains with known toxicities to termites, a red pigment-producing strain, HDZK-BYSB107, was isolated from Chamaecyparis lawsoniana, which grows in Oregon, USA. Strain HDZK-BYSB107 was identified as Serratia marcescens subsp. lawsoniana. The red pigment was identified as prodigiosin using ultraviolet absorption, LC-MS, and 1H-NMR spectroscopy. The bacterial prodigiosin had an inhibitory effect on both Gram-negative and Gram-positive bacteria. The main objective of this study was to explore the anticancer activities and mechanism of strain HDZK-BYSB107 prodigiosin by using human choriocarcinoma (JEG3) and prostate cancer cell lines (PC3) in vitro and JEG3 and PC3 tumor-bearing nude mice in vivo. In vitro anticancer activities showed that the bacterial prodigiosin induced apoptosis in JEG3 cells. In vivo anticancer activities indicated that the prodigiosin significantly inhibited the growth of JEG3 and PC3 cells, and the inhibitory activity was dose and time dependent. The anticancer efficacy of the bacterial prodigiosin on JEG3 and PC3 cells, JEG3 and PC3 tumor exhibited a correlation with the down regulation of the inhibitor of IAP family, including XIAP, cIAP-1 and cIAP-2, and the activation of caspase-9 and caspase-3 accompanied by proteolytic degradation of poly (ADP-ribose)-polymerase. The expressions of P53 and Bax/Bcl-2 in JEG3 and PC3 cells were significantly higher than in untreated groups. Our results indicated that the bacterial prodigiosin extracted from C. lawsoniana is a promising molecule due to its potential for therapeutic applications.
Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction

Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: Given reactants and reagents, predict the products. Similar to other work, we treat reaction prediction as a machine translation problem between simplified molecular-input line-entry system (SMILES) strings (a text-based representation) of reactants, reagents, and the products. We show that a multihead attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark data set. Molecular Transformer makes predictions by inferring the correlations between the presence and absence of chemical motifs in the reactant, reagent, and product present in the data set. Our model requires no handcrafted rules and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying whether a prediction is correct. Furthermore, we show that the model is able to handle inputs without a reactant–reagent split and including stereochemistry, which makes our method universally applicable.
The Effect of Saphenous Vein Ablation on Combined Segmental Popliteal Vein Reflux

Background This study examined the role of superficial vein surgery in patients with combined superficial venous reflux and segmental popliteal vein reflux. Methods We retrospectively reviewed 42 limbs in 38 patients with combined superficial venous reflux and segmental popliteal vein reflux who underwent saphenous vein ablation between January 2014 and February 2017. Patients underwent outpatient follow-up duplex ultrasonography at 3, 6, and 12 months postoperatively. Resolution of deep vein reflux was defined as reversed blood flow in a popliteal segment for less than 1.0 second and a decrease in the reflux time of more than 20% of the preoperative reflux time. Results The mean follow-up period was 9 months (range, 3–23 months). Saphenous vein ablations were performed by stripping in 24 limbs and radiofrequency ablation in 18 limbs. Preoperative segmental popliteal vein reflux resolved in 21 of the 42 limbs (50%). Conclusion This study demonstrated that superficial venous surgery corrected segmental popliteal vein reflux in 50% of limbs with combined superficial venous reflux and segmental popliteal vein reflux. Other prospective studies are necessary to elucidate the etiology of the non-reversible cases.
Broadening horizons: Sample diversity and socioecological theory are essential to the future of psychological science

The present lack of sample diversity and ecological theory in psychological science fundamentally limits generalizability and obstructs scientific progress. A focus on the role of socioecology in shaping the evolution of morphology, physiology, and behavior has not yet been widely applied toward psychology. To date, evolutionary approaches to psychology have focused more on finding universals than explaining variability. However, contrasts between small-scale, kin-based rural subsistence societies and large-scale urban, market-based populations, have not been well appreciated. Nor has the variability within high-income countries, or the socioeconomic and cultural transformations affecting even the most remote tribal populations today. Elucidating the causes and effects of such broad changes on psychology and behavior is a fundamental concern of the social sciences; expanding study participants beyond students and other convenience samples is necessary to improve understanding of flexible psychological reaction norms among and within populations. Here I highlight two examples demonstrating how socioecological variability can help explain psychological trait expression: (i) the role of environmental harshness and unpredictability on shaping time preference and related traits, such as impulsivity, vigilance, and self-efficacy; and (ii) the effects of industrialization, market integration, and niche complexity on personality structure. These cases illustrate how appropriate theory can be a powerful tool to help determine choices of diverse study populations and improve the social sciences.
Real-Time Temporal Frequency Detection in FPGA Using Event-Based Vision Sensor

A dynamic vision sensor (DVS) is a new type of vision sensor in which each pixel acts as a motion sensor and generates highly time-accurate events when it detects movement in the scene. The high temporal precision of these types of vision sensors allows the extraction of different low-level temporal features, which is not possible when using a frame-based camera. Hierarchical vision-processing systems use low-level features to recognize a higher level of abstraction. One of the lowlevel features that can be extracted with DVS is the temporal frequency. This feature can be used along with other visual features for more accurate object recognition when the object has rotating parts, such as a quadcopter. This work is an extension of our previous work, wherein we proposed an algorithm to extract this temporal low-level feature by using a DVS. In this work, we proposed a digital circuit with a small footprint to extract the frequency of rotating objects in real time with very low latency. We have synthesized the digital circuit in Spartan-6 field-programmable gate array (FPGA) and also in UMC 180-nm technology to measure the performance, power consumption, and occupied area. MATLAB and Verilog codes for this work are available for academic purposes upon request.
Distinct effects of (R)‐modafinil and its (R)‐ and (S)‐fluoro‐analogs on mesolimbic extracellular dopamine assessed by voltammetry and microdialysis in rats

Psychostimulant use disorders remain an unabated public health concern worldwide, but no FDA approved medications are currently available for treatment. Modafinil (MOD), like cocaine, is a dopamine reuptake inhibitor and one of the few drugs evaluated in clinical trials that has shown promise for the treatment of cocaine or methamphetamine use disorders in some patient subpopulations. Recent structure–activity relationship and preclinical studies on a series of MOD analogs have provided insight into modifications of its chemical structure that may lead to advancements in clinical efficacy. Here, we have tested the effects of the clinically available (R)‐enantiomer of MOD on extracellular dopamine levels in the nucleus accumbens shell, a mesolimbic dopaminergic projection field that plays significant roles in various aspects of psychostimulant use disorders, measured in vivo by fast‐scan cyclic voltammetry and by microdialysis in Sprague‐Dawley rats. We have compared these results with those obtained under identical experimental conditions with two novel and enantiopure bis(F) analogs of MOD, JBG1‐048 and JBG1‐049. The results show that (R)‐modafinil (R‐MOD), JBG1‐048, and JBG1‐049, when administered intravenously with cumulative drug‐doses, will block the dopamine transporter and reduce the clearance rate of dopamine, increasing its extracellular levels. Differences among the compounds in their maximum stimulation of dopamine levels, and in their time course of effects were also observed. These data highlight the mechanistic underpinnings of R‐MOD and its bis(F) analogs as pharmacological tools to guide the discovery of novel medications to treat psychostimulant use disorders.