Graph Neural Machine: A New Model for Learning with Tabular Data

In recent years, there has been a growing interest in mapping data from different domains to graph structures. Among others, neural network models such as the multi-layer perceptron (MLP) can be modeled as graphs. In fact, MLPs can be represented as directed acyclic graphs. Graph neural networks (GNNs) have recently become the standard tool for performing machine learning tasks on graphs. In this work, we show that an MLP is equivalent to an asynchronous message passing GNN model which operates on the MLP's graph representation. We then propose a new machine learning model for tabular data, the so-called Graph Neural Machine (GNM), which replaces the MLP's directed acyclic graph with a nearly complete graph and which employs a synchronous message passing scheme. We show that a single GNM model can simulate multiple MLP models. We evaluate the proposed model in several classification and regression datasets. In most cases, the GNM model outperforms the MLP architecture

Strand specific RNA-sequencing and membrane lipid profiling reveals growth phase-dependent cold stress response mechanisms in Listeria monocytogenes

The human pathogen Listeria monocytogenes continues to pose a challenge in the food industry, where it is known to contaminate ready-to-eat foods and grow during refrigerated storage. Increased knowledge of the cold-stress response of this pathogen will enhance the ability to control it in the food-supply-chain. This study utilized strand-specific RNA sequencing and whole cell fatty acid (FA) profiling to characterize the bacterium's cold stress response. RNA and FAs were extracted from a cold-tolerant strain at five time points between early lag phase and late stationary-phase, both at 4°C and 20°C. Overall, more genes (1.3×) were suppressed than induced at 4°C. Late stationary-phase cells exhibited the greatest number (n = 1,431) and magnitude (>1,000-fold) of differentially expressed genes (>2-fold, p<0.05) in response to cold. A core set of 22 genes was upregulated at all growth phases, including nine genes required for branched-chain fatty acid (BCFA) synthesis, the osmolyte transporter genes opuCBCD, and the internalin A and D genes. Genes suppressed at 4°C were largely associated with cobalamin (B12) biosynthesis or the production/export of cell wall components. Antisense transcription accounted for up to 1.6% of total mapped reads with higher levels (2.5×) observed at 4°C than 20°C. The greatest number of upregulated antisense transcripts at 4°C occurred in early lag phase, however, at both temperatures, antisense expression levels were highest in late stationary-phase cells. Cold-induced FA membrane changes included a 15% increase in the proportion of BCFAs and a 15% transient increase in unsaturated FAs between lag and exponential phase. These increases probably reduced the membrane phase transition temperature until optimal levels of BCFAs could be produced. Collectively, this research provides new information regarding cold-induced membrane composition changes in L. monocytogenes, the growth-phase dependency of its cold-stress regulon, and the active roles of antisense transcripts in regulating its cold stress response

Isolation and characterization of bacteriophages for controlling Rhizobium radiobacter – causing stem and crown gall of highbush blueberry

IntroductionStem and crown gall disease caused by the plant pathogen Rhizobium radiobacter has a significant impact on highbush blueberry (Vaccinium corymbosum) production. Current methods for controlling the bacterium are limited. Lytic phages, which can specifically target host bacteria, have been widely gained interest in agriculture.MethodsIn this study, 76 bacteriophages were recovered from sewage influent and screened for their inhibitory effect against Rhizobium spp. The phages were genetically characterized through whole-genome sequencing, and their lytic cycle was confirmed.ResultsFive potential candidate phages (isolates IC12, IG49, AN01, LG08, and LG11) with the ability to lyse a broad range of hosts were chosen and assessed for their morphology, environmental stability, latent period, and burst size. The morphology of these selected phages revealed a long contractile tail under transmission electron microscopy. Single-step growth curves displayed that these phages had a latent period of 80–110 min and a burst size ranging from 8 to 33 phages per infected cell. None of these phages contained any antimicrobial resistance or virulence genes in their genomes. Subsequently, a combination of two-, three- and four-phage cocktails were formulated and tested for their efficacy in a broth system. A three-phage cocktail composed of the isolates IC12, IG49 and LG08 showed promising results in controlling a large number of R. radiobacter strains in vitro. In a soil/peat-based model, the three-phage cocktail was tested against R. radiobacter PL17, resulting in a significant reduction (p &lt; 0.05) of 2.9 and 1.3 log10 CFU/g after 24 and 48 h of incubation, respectively.DiscussionThese findings suggest that the three-phage cocktail (IC12, IG49 and LG08) has the potential to serve as a proactive antimicrobial solution for controlling R. radiobacter on blueberry

Broadband spectral combining of three pulse-shaped fiber amplifiers with 42fs compressed pulse duration.

We demonstrate ultra-broadband spectral combining of ultrashort pulses from Yb-doped fiber amplifiers, with coherently spectrally synthesized pulse shaping, to achieve tens-of-fs pulses. This method can fully compensate for gain narrowing and high order dispersion over broad bandwidth. We produce 42fs pulses by spectrally synthesizing three chirped-pulse fiber amplifiers and two programmable pulse shapers across an 80nm overall bandwidth. To the best of our knowledge, this is the shortest pulse duration achieved from a spectrally combined fiber system at one-micron wavelength. This work provides a path toward high-energy, tens-of-fs fiber chirped-pulse amplification systems

A fully-mapped and energy-efficient FPGA accelerator for dual-function AI-based analysis of ECG

In this paper, a fully-mapped field programmable gate array (FPGA) accelerator is proposed for artificial intelligence (AI)-based analysis of electrocardiogram (ECG). It consists of a fully-mapped 1-D convolutional neural network (CNN) and a fully-mapped heart rate estimator, which constitute a complementary dual-function analysis. The fully-mapped design projects each layer of the 1-D CNN to a hardware module on an Intel Cyclone V FPGA, and a virtual flatten layer is proposed to effectively bridge the feature extraction layers and fully-connected layer. Also, the fully-mapped design maximizes computational parallelism to accelerate CNN inference. For the fully-mapped heart rate estimator, it performs pipelined transformations, self-adaptive threshold calculation, and heartbeat count on the FPGA, without multiplexed usage of hardware resources. Furthermore, heart rate calculation is elaborately analyzed and optimized to remove division and acceleration, resulting in an efficient method suitable for hardware implementation. According to our experiments on 1-D CNN, the accelerator can achieve 43.08× and 8.38× speedup compared with the software implementations on ARM-Cortex A53 quad-core processor and Intel Core i7-8700 CPU, respectively. For the heart rate estimator, the hardware implementations are 25.48× and 1.55× faster than the software implementations on the two aforementioned platforms. Surprisingly, the accelerator achieves an energy efficiency of 63.48 GOPS/W, which obviously surpasses existing studies. Considering its power consumption is only 67.74 mW, it may be more suitable for resource-limited applications, such as wearable and portable devices for ECG monitoring

Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study

ObjectiveThe purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling.MethodsA total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)&gt;600 days n=152) noninert nodules (VDT&lt;600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model &lt;sn&lt;/sn&gt;&gt;(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R2(determination coefficient).ResultsThe accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients’ first examination and consultationConclusionThe INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules

A semi-quantitative upconversion nanoparticle-based immunochromatographic assay for SARS-CoV-2 antigen detection

The unprecedented public health and economic impact of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been met with an equally unprecedented scientific response. Sensitive point-of-care methods to detect SARS-CoV-2 antigens in clinical specimens are urgently required for the rapid screening of individuals with viral infection. Here, we developed an upconversion nanoparticle-based lateral flow immunochromatographic assay (UCNP-LFIA) for the high-sensitivity detection of SARS-CoV-2 nucleocapsid (N) protein. A pair of rabbit SARS-CoV-2 N-specific monoclonal antibodies was conjugated to UCNPs, and the prepared UCNPs were then deposited into the LFIA test strips for detecting and capturing the N protein. Under the test conditions, the limit of detection (LOD) of UCNP-LFIA for the N protein was 3.59 pg/mL, with a linear range of 0.01–100 ng/mL. Compared with that of the current colloidal gold-based LFIA strips, the LOD of the UCNP-LFIA-based method was increased by 100-fold. The antigen recovery rate of the developed method in the simulated pharyngeal swab samples ranged from 91.1 to 117.3%. Furthermore, compared with the reverse transcription-polymerase chain reaction, the developed UCNP-LFIA method showed a sensitivity of 94.73% for 19 patients with COVID-19. Thus, the newly established platform could serve as a promising and convenient fluorescent immunological sensing approach for the efficient screening and diagnosis of COVID-19

Genome-wide association study on serum alkaline phosphatase levels in a Chinese population

Background: Serum alkaline phosphatase (ALP) is a complex phenotype influenced by both genetic and environmental factors. Recent Genome-Wide Association Studies (GWAS) have identified several loci affecting ALP levels; however, such studies in Chinese populations are limited. We performed a GWAS analyzing the association between 658,288 autosomal SNPs and serum ALP in 1,461 subjects, and replicated the top SNPs in an additional 8,830 healthy Chinese Han individuals. The interactions between significant locus and environmental factors on serum ALP levels were further investigated. Results: The association between ABO locus and serum ALP levels was replicated (P = 2.50 × 10-21, 1.12 × 10-56 and 2.82 × 10-27 for SNP rs8176720, rs651007 and rs7025162 on ABO locus, respectively). SNP rs651007 accounted for 2.15% of the total variance of serum ALP levels independently of the other 2 SNPs. When comparing our findings with previously published studies, ethnic differences were observed across populations. A significant interaction between ABO rs651007 and overweight and obesity was observed (FDR for interaction was 0.036); for individuals with GG genotype, those with normal weight and those who were overweight or obese have similar serum ALP concentrations; minor allele A of rs651007 remarkably reduced serum ALP levels, but this effect was attenuated in overweight and obese individuals. Conclusions: Our findings indicate that ABO locus is a major determinant for serum ALP levels in Chinese Han population. Overweight and obesity modifies the effect of ABO locus on serum ALP concentrations

Unraveling the pathogenic potential of the Pentatrichomonas hominis PHGD strain: impact on IPEC-J2 cell growth, adhesion, and gene expression

Pentatrichomonas hominis, a flagellated parasitic protozoan, predominantly infects the mammalian digestive tract, often causing symptoms such as abdominal pain and diarrhea. However, studies investigating its pathogenicity are limited, and the mechanisms underlying P. hominis-induced diarrhea remain unclear. Establishing an in vitro cell model for P. hominis infection is imperative. This study investigated the interaction between P. hominis and IPEC-J2 cells and its impact on parasite growth, adhesion, morphology, and cell viability. Co-cultivation of P. hominis with IPEC-J2 cells resulted in exponential growth of the parasite, with peak densities reaching approximately 4.8 × 105 cells/mL and 1.2 × 106 cells/mL at 48 h for initial inoculation concentrations of 104 cells/mL and 105 cells/mL, respectively. The adhesion rate of P. hominis to IPEC-J2 cells reached a maximum of 93.82% and 86.57% at 24 h for initial inoculation concentrations of 104 cells/mL and 105 cells/mL, respectively. Morphological changes in IPEC-J2 cells co-cultivated with P. hominis were observed, manifesting as elongated and irregular shapes. The viability of IPEC-J2 cells exhibited a decreasing trend with increasing P. hominis concentration and co-cultivation time. Additionally, the mRNA expression levels of IL-6, IL-8, and TNF-α were upregulated, whereas those of CAT and CuZn-SOD were downregulated. These findings provide quantitative evidence that P. hominis can promote its growth by adhering to IPEC-J2 cells, inducing morphological changes, reducing cell viability, and triggering inflammatory responses. Further in vivo studies are warranted to confirm these results and enhance our understanding of P. hominis infection