Binarized neural network of diode array with high concordance to vector-matrix multiplication.

In this study, a binarized neural network (BNN) of silicon diode arrays achieved vector-matrix multiplication (VMM) between the binarized weights and inputs in these arrays. The diodes that operate in a positive-feedback loop in their p+-n-p-n+ device structure possess steep switching and bistable characteristics with an extremely low subthreshold swing (below 1 mV) and a high current ratio (approximately 108). Moreover, the arrays show a self-rectifying functionality and an outstanding linearity by an R-squared value of 0.99986, which allows to compose a synaptic cell with a single diode. A 2 × 2 diode array can perform matrix multiply-accumulate operations for various binarized weight matrix cases with some input vectors, which is in high concordance with the VMM, owing to the high reliability and uniformity of the diodes. Moreover, the disturbance-free, nondestructive readout, and semi-permanent holding characteristics of the diode arrays support the feasibility of implementing the BNN.

Assessment of the accumulation level and ecological risk of heavy metals in surface sediments of Bong Mieu River, Quang Nam Province, Vietnam.

This study assessed the accumulation levels and ecological risks associated with seven heavy metals (As, Pb, Cd, Hg, Cu, Cr, Zn) in the surface sediments of the Bong Mieu River in Quang Nam Province, Vietnam. The sampling encompassed 10 locations (S1-S10), considering areas both impacted and less impacted by gold mining activities. The findings revealed elevated levels of heavy metal pollution and associated ecological risks attributable to gold mining. Heavy metal content varied within specific ranges: As (70.6-341.2 mg/kg), Pb (216.3-504.1 mg/kg), Hg (0.138-0.252 mg/kg), Cd (0.91-1.51mg/kg), Cu (18.3-45.5 mg/kg), Cr (10.5-19.1 mg/kg), and Zn (49.3-84.1 mg/kg). Among these elements, Hg, Cu, Cr, Zn, and Cd adhered to the acceptable limits of VNTR 43:2017/MONRE (VNTR 43:2017/MONRE: National Technical Regulation/Ministry of Natural Resources and Environment of Vietnam). However, As and Pb content at all locations exceeded these limits significantly, with As being 4.1-20 times higher and Pb 2.3-5.5 times higher. The pollution of Pb and As was attributed to waste discharge from gold mining activities, which carry substantial amounts of these metals in various forms. The Igeo indicated heavy pollution of As and Pb in the sediments. Ecological risk factors were ranked as follows: E r i (As) > E r i (Pb) > E r i (Hg) > E r i (Cd) > E r i (Cu) > E r i (Cr) > E r i (Zn). The potential ecological risk (RI) due to combined heavy metal impact varied across locations, with S2 > S8 > S9 > S6 > S7 > S10 > S1 > S3 > S4 > S5, exhibiting low to moderate risk (RI values ranging from 73.4 to 252.8). The study area demonstrated high contamination levels for As and Pb, coupled with low to moderate potential ecological risks.

Reliable Memristor Crossbar Array Based on 2D Layered Nickel Phosphorus Trisulfide for Energy-Efficient Neuromorphic Hardware.

Designing reliable and energy-efficient memristors for artificial synaptic arrays in neuromorphic computing beyond von Neumann architecture remains a challenge. Here, memristors based on emerging layered nickel phosphorus trisulfide (NiPS3 ) are reported that exhibit several favorable characteristics, including uniform bipolar nonvolatile switching with small operating voltage (<1 V), fast switching speed (< 20 ns), high On/Off ratio (>102 ), and the ability to achieve programmable multilevel resistance states. Through direct experimental evidence using transmission electron microscopy and energy dispersive X-ray spectroscopy, it is revealed that the resistive switching mechanism in the Ti/NiPS3 /Au device is related to the formation and dissolution of Ti conductive filaments. Intriguingly, further investigation into the microstructural and chemical properties of NiPS3 suggests that the penetration of Ti ions is accompanied by the drift of phosphorus-sulfur ions, leading to induced P/S vacancies that facilitate the formation of conductive filaments. Furthermore, it is demonstrated that the memristor, when operating in quasi-reset mode, effectively emulates long-term synaptic weight plasticity. By utilizing a crossbar array, multipattern memorization and multiply-and-accumulate (MAC) operations are successfully implemented. Moreover, owing to the highly linear and symmetric multiple conductance states, a high pattern recognition accuracy of ≈96.4% is demonstrated in artificial neural network simulation for neuromorphic systems.

Endometrial cancer intraoperative sentinel lymph node identification can effectively guide treatment.

OBJECTIVE: To explore the significance of intraoperative sentinel lymph node (SLN) identification in endometrial cancer. METHODS: We retrospectively analyzed the clinical data of 56 patients with intraoperative SLN recognition (group A) and 50 patients without intraoperative SLN recognition (group B). SLN and pelvic abdominal lymph node distribution, SLN recognition rate, SLN recognition effect, mortality, the incidence of adverse events, and cumulative survival rate were statistically analyzed. RESULTS: SLN were identified and removed in 41 of the 56 patients, with a recognition rate of 82.14% (46/56). The sensitivity of SLN was 83.72%, the specificity was 84.62%, and the negative predictive value was 61.11%. There were 15 patients with no SLN metastasis found in the pathological examination during the operation, among which two patients with poorly differentiated adenocarcinoma and clinical stage II patients underwent immunohistochemical staining, and three patients showed SLN micro-metastasis but no cancer tissue metastasis in the lymph node dissection. There was no significant difference in the incidence of total adverse events between group A and group B (P>0.05). The cumulative survival rate of group A was higher than that of group B (P=0.018). CONCLUSION: Intraoperative SLE identification can avoid false negative results, is safe and feasible, and can prolong the survival time of patients with endometrial cancer.

FinFET 6T-SRAM All-Digital Compute-in-Memory for Artificial Intelligence Applications: An Overview and Analysis.

Artificial intelligence (AI) has revolutionized present-day life through automation and independent decision-making capabilities. For AI hardware implementations, the 6T-SRAM cell is a suitable candidate due to its performance edge over its counterparts. However, modern AI hardware such as neural networks (NNs) access off-chip data quite often, degrading the overall system performance. Compute-in-memory (CIM) reduces off-chip data access transactions. One CIM approach is based on the mixed-signal domain, but it suffers from limited bit precision and signal margin issues. An alternate emerging approach uses the all-digital signal domain that provides better signal margins and bit precision; however, it will be at the expense of hardware overhead. We have analyzed digital signal domain CIM silicon-verified 6T-SRAM CIM solutions, after classifying them as SRAM-based accelerators, i.e., near-memory computing (NMC), and custom SRAM-based CIM, i.e., in-memory-computing (IMC). We have focused on multiply and accumulate (MAC) as the most frequent operation in convolution neural networks (CNNs) and compared state-of-the-art implementations. Neural networks with low weight precision, i.e., <12b, show lower accuracy but higher power efficiency. An input precision of 8b achieves implementation requirements. The maximum performance reported is 7.49 TOPS at 330 MHz, while custom SRAM-based performance has shown a maximum of 5.6 GOPS at 100 MHz. The second part of this article analyzes the FinFET 6T-SRAM as one of the critical components in determining overall performance of an AI computing system. We have investigated the FinFET 6T-SRAM cell performance and limitations as dictated by the FinFET technology-specific parameters, such as sizing, threshold voltage (Vth), supply voltage (VDD), and process and environmental variations. The HD FinFET 6T-SRAM cell shows 32% lower read access time and 1.09 times better leakage power as compared with the HC cell configuration. The minimum achievable supply voltage is 600 mV without utilization of any read- or write-assist scheme for all cell configurations, while temperature variations show noise margin deviation of up to 22% of the nominal values.

Energy-Efficient Hardware Implementation of Fully Connected Artificial Neural Networks Using Approximate Arithmetic Blocks.

In this paper, we explore efficient hardware implementation of feedforward artificial neural networks (ANNs) using approximate adders and multipliers. Due to a large area requirement in a parallel architecture, the ANNs are implemented under the time-multiplexed architecture where computing resources are re-used in the multiply accumulate (MAC) blocks. The efficient hardware implementation of ANNs is realized by replacing the exact adders and multipliers in the MAC blocks by the approximate ones taking into account the hardware accuracy. Additionally, an algorithm to determine the approximate level of multipliers and adders due to the expected accuracy is proposed. As an application, the MNIST and SVHN databases are considered. To examine the efficiency of the proposed method, various architectures and structures of ANNs are realized. Experimental results show that the ANNs designed using the proposed approximate multiplier have a smaller area and consume less energy than those designed using previously proposed prominent approximate multipliers. It is also observed that the use of both approximate adders and multipliers yields, respectively, up to 50% and 10% reduction in energy consumption and area of the ANN design with a small deviation or better hardware accuracy when compared to the exact adders and multipliers.

An artificial neural network chip based on two-dimensional semiconductor.

Recently, research on two-dimensional (2D) semiconductors has begun to translate from the fundamental investigation into rudimentary functional circuits. In this work, we unveil the first functional MoS2 artificial neural network (ANN) chip, including multiply-and-accumulate (MAC), memory and activation function circuits. Such MoS2 ANN chip is realized through fabricating 818 field-effect transistors (FETs) on a wafer-scale and high-homogeneity MoS2 film, with a gate-last process to realize top gate structured FETs. A 62-level simulation program with integrated circuit emphasis (SPICE) model is utilized to design and optimize our analog ANN circuits. To demonstrate a practical application, a tactile digit sensing recognition was demonstrated based on our ANN circuits. After training, the digit recognition rate exceeds 97%. Our work not only demonstrates the protentional of 2D semiconductors in wafer-scale integrated circuits, but also paves the way for its future application in AI computation.

Silicon-Based Metastructure Optical Scattering Multiply-Accumulate Computation Chip.

Optical neural networks (ONN) have become the most promising solution to replacing electronic neural networks, which have the advantages of large bandwidth, low energy consumption, strong parallel processing ability, and super high speed. Silicon-based micro-nano integrated photonic platforms have demonstrated good compatibility with complementary metal oxide semiconductor (CMOS) processing. Therefore, without completely changing the existing silicon-based fabrication technology, optoelectronic hybrid devices or all-optical devices of better performance can be achieved on such platforms. To meet the requirements of smaller size and higher integration for silicon photonic computing, the topology of a four-channel coarse wavelength division multiplexer (CWDM) and an optical scattering unit (OSU) are inversely designed and optimized by Lumerical software. Due to the random optical power splitting ratio and incoherency, the intensities of different input signals from CWDM can be weighted and summed directly by the subsequent OSU to accomplish arbitrary multiply-accumulate (MAC) operations, therefore supplying the core foundation for scattering ONN architecture.

Exogenous Indole-3-Acetic Acid Induced Ethanol Tolerance in Phylogenetically Diverse Saccharomycetales Yeasts.

Indole-3-acetic acid (IAA) is an exogenous growth regulatory signal that is produced by plants and various microorganisms. Microorganisms have been suggested to cross-communicate with each other through IAA-mediated signaling mechanisms. The IAA-induced tolerance response has been reported in several microorganisms, but has not yet been described in Saccharomycetales yeasts. In the present study, three common stressors (heat, osmotic pressure, and ethanol) were examined in relation to the influence of a pretreatment with IAA on stress tolerance in 12 different lineages of Saccharomyces cerevisiae. The pretreatment with IAA had a significant effect on the induction of ethanol tolerance by reducing the doubling time of S. cerevisiae growth without the pretreatment. However, the pretreatment did not significantly affect the induction of thermo- or osmotolerance. The IAA pretreatment decreased the lethal effects of ethanol on S. cerevisiae cells. Although yeasts produce ethanol to outcompete sympatric microorganisms, IAA is not a byproduct of this process. Nevertheless, the accumulation of IAA indicates an increasing number of microorganisms, and, thus, greater competition for resources. Since the "wine trait" is shared by both phylogenetically related and distinct lineages in Saccharomycetales, we conclude that IAA-induced ethanol tolerance is not specific to S. cerevisiae; it may be widely detected in both pre-whole genome duplication (WGD) and post-WGD yeasts belonging to several genera of Saccharomycetales.

Wafer-Scale 2D Hafnium Diselenide Based Memristor Crossbar Array for Energy-Efficient Neural Network Hardware.

Memristor crossbar with programmable conductance could overcome the energy consumption and speed limitations of neural networks when executing core computing tasks in image processing. However, the implementation of crossbar array (CBA) based on ultrathin 2D materials is hindered by challenges associated with large-scale material synthesis and device integration. Here, a memristor CBA is demonstrated using wafer-scale (2-inch) polycrystalline hafnium diselenide (HfSe2 ) grown by molecular beam epitaxy, and a metal-assisted van der Waals transfer technique. The memristor exhibits small switching voltage (0.6 V), low switching energy (0.82 pJ), and simultaneously achieves emulation of synaptic weight plasticity. Furthermore, the CBA enables artificial neural network with a high recognition accuracy of 93.34%. Hardware multiply-and-accumulate (MAC) operation with a narrow error distribution of 0.29% is also demonstrated, and a high power efficiency of greater than 8-trillion operations per second per Watt is achieved. Based on the MAC results, hardware convolution image processing can be performed using programmable kernels (i.e., soft, horizontal, and vertical edge enhancement), which constitutes a vital function for neural network hardware.

Research and Progress on the Mechanism of Iron Transfer and Accumulation in Rice Grains.

Iron (Fe) is one of the most important micronutrients for organisms. Currently, Fe deficiency is a growing nutritional problem and is becoming a serious threat to human health worldwide. A method that could help alleviate this "hidden hunger" is increasing the bioavailable Fe concentrations in edible tissues of major food crops. Therefore, understanding the molecular mechanisms of Fe accumulation in different crop tissues will help to develop crops with higher Fe nutritional values. Biofortification significantly increases the concentration of Fe in crops. This paper considers the important food crop of rice (Oryza sativa L.) as an example and highlights recent research advances on the molecular mechanisms of Fe uptake and allogeneic uptake in different tissues of rice. In addition, different approaches to the biofortification of Fe nutrition in rice and their outcomes are described and discussed. To address the problems that occur during the development and application of improving nutritional Fe in rice, technical strategies and long-term solutions are also proposed as a reference for the future improvement of staple food nutrition with micronutrients.

Multi-Omics Analysis of Gene and Protein Candidates Possibly Related to Tetrodotoxin Accumulation in the Skin of Takifugu flavidus.

Pufferfish is increasingly regarded by many as a delicacy. However, the tetrodotoxin (TTX) that accumulates in its body can be lethal upon consumption by humans. TTX is known to mainly accumulate in pufferfish skin, but the accumulation mechanisms are poorly understood. In this study, we aimed to explore the possible mechanism of TTX accumulation in the skin of the pufferfish Takifugu flavidus following treatment with TTX. Through liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, we detected 37.3% of toxin accumulated in the skin at the end of the rearing period (168 h). Transcriptome and proteome analyses revealed the mechanism and pathways of TTX accumulation in the skin of T. flavidus in detail. Gene ontology and the Kyoto Encyclopedia of Genes and Genomes analyses strongly suggest that cardiac muscle contraction and adrenergic signaling in cardiomyocyte pathways play an important role in TTX accumulation. Moreover, some upregulated and downregulated genes, which were determined via RNA-Seq, were verified with qPCR analysis. This study is the first to use multi-omics profiling data to identify novel regulatory network mechanisms of TTX accumulation in the skin of pufferfish.

Numerical behavior of NVIDIA tensor cores.

We explore the floating-point arithmetic implemented in the NVIDIA tensor cores, which are hardware accelerators for mixed-precision matrix multiplication available on the Volta, Turing, and Ampere microarchitectures. Using Volta V100, Turing T4, and Ampere A100 graphics cards, we determine what precision is used for the intermediate results, whether subnormal numbers are supported, what rounding mode is used, in which order the operations underlying the matrix multiplication are performed, and whether partial sums are normalized. These aspects are not documented by NVIDIA, and we gain insight by running carefully designed numerical experiments on these hardware units. Knowing the answers to these questions is important if one wishes to: (1) accurately simulate NVIDIA tensor cores on conventional hardware; (2) understand the differences between results produced by code that utilizes tensor cores and code that uses only IEEE 754-compliant arithmetic operations; and (3) build custom hardware whose behavior matches that of NVIDIA tensor cores. As part of this work we provide a test suite that can be easily adapted to test newer versions of the NVIDIA tensor cores as well as similar accelerators from other vendors, as they become available. Moreover, we identify a non-monotonicity issue affecting floating point multi-operand adders if the intermediate results are not normalized after each step.

A PPR Protein ACM1 Is Involved in Chloroplast Gene Expression and Early Plastid Development in Arabidopsis.

Chloroplasts cannot develop normally without the coordinated action of various proteins and signaling connections between the nucleus and the chloroplast genome. Many questions regarding these processes remain unanswered. Here, we report a novel P-type pentatricopeptide repeat (PPR) factor, named Albino Cotyledon Mutant1 (ACM1), which is encoded by a nuclear gene and involved in chloroplast development. Knock-down of ACM1 transgenic plants displayed albino cotyledons but normal true leaves, while knock-out of the ACM1 gene in seedlings was lethal. Fluorescent protein analysis showed that ACM1 was specifically localized within chloroplasts. PEP-dependent plastid transcript levels and splicing efficiency of several group II introns were seriously affected in cotyledons in the RNAi line. Furthermore, denaturing gel electrophoresis and Western blot experiments showed that the accumulation of chloroplast ribosomes was probably damaged. Collectively, our results indicate ACM1 is indispensable in early chloroplast development in Arabidopsis cotyledons.

Distribution of Melanin Pigmentation in 33 Organs of Thai Black-Bone Chickens (Gallus gallus domesticus).

The black-bone chicken (Gallus gallus domesticus) is a breed of chicken that is commonly found in Thailand. This breed is known for having a number of black colored organs. Consumers have been notably attracted to the black-bone chicken breed for the characteristic darkness that is observed in many of its organs. However, the degree of darkness in all organs of the black-bone chicken is still in question. Importantly, there have not yet been any published reports on the distribution of melanin pigment in the organs of the black-bone chicken. This research study aims to examine the distribution of the melanin pigment in 33 organs of the Thai black-bone chicken. Ten black-bone chickens (five male, five female) were included in this study. Thirty-two organs including the brain, spinal cord, sciatic nerve, larynx, trachea, syrinx, lungs, heart, pericardium, aorta, brachial vein, kidney, cloaca, oviduct, testis, gastrocnemius muscle, femur, tongue, esophagus, crop, proventriculus, gizzard, duodenum, jejunum, ileum, cecum, pancreas, liver, gall bladder, omentum, abdominal fat, spleen, and skin were examined in this study. Histological sections taken from tissue samples of each of these organs were studied. The findings revealed that the presence of the melanin pigment was not significantly different (p > 0.005) between male and female specimens. Notably, the liver was the only organ in which the melanin pigment had not accumulated. Consequently, there was not a uniform pattern of melanin pigment accumulation throughout the organs of the chickens. The melanin pigment was present in all of the tissue layers of most organs, while the melanin pigment was found in only specific layers of some of the organs. In conclusion, the distribution of melanin pigmentation in the organs of each of the animals in this study was found to be different. However, in some tissue samples, such as those obtained from the liver, no accumulation of the melanin pigment was observed.

Determinants of soil organic carbon sequestration and its contribution to ecosystem carbon sinks of planted forests.

The area of forest established through afforestation/reforestation has been increasing on a global scale, which is particularly important as these planted forests attenuate climate change by sequestering carbon. However, the determinants of soil organic carbon (SOC) sequestration and their contribution to the ecosystem carbon sink of planted forests remain uncertain. By using globally distributed data extracted from 154 peer-reviewed publications and a total of 355 sampling points, we investigated above-ground biomass carbon (ABC) sequestration and SOC sequestration across three different climatic zones (tropical, warm temperate, and cold temperate) through correlation analysis, regression models, and structural equation modeling (SEM). We found that the proportion of SOC sequestration in the ecosystem C sequestration averaged 14.1% globally, being the highest (27.0%) in the warm temperate and the lowest (10.7%) in the tropical climatic zones. The proportion was mainly affected by latitude. The sink rate of ABC (RABC ) in tropical climates (2.48 Mg C ha-1  year-1 ) and the sink rate of SOC (RSOC ) in warm temperate climates (0.96 Mg C ha-1  year-1 ) were higher than other climatic zones. The main determinants of RSOC were the number of frost-free days, latitude, mean annual precipitation (MAP), and SOC density (SOCD) at the initial observation; however, these variables depended on the climatic zone. According to the SEM, frost-free period, mean annual temperature (MAT) and MAP are the dominant driving factors affecting RSOC in Chinese plantations. MAT has a positive effect on RSOC , and global warming may increase RSOC of temperate plantations in China. Our findings highlight the determinants of SOC sequestration and quantitatively reveal the substantial global contribution of SOC sequestration to ecosystem carbon sink provided by planted forests. Our results help managers identify and control key factors to increase carbon sequestration in forest ecosystems.

Combined Edge- and Stixel-based Object Detection in 3D Point Cloud.

Environment perception is critical for feasible path planning and safe driving for autonomous vehicles. Perception devices, such as camera, LiDAR (Light Detection and Ranging), IMU(Inertial Measurement Unit), etc., only provide raw sensing data with no identification of vital objects, which is insufficient for autonomous vehicles to perform safe and efficient self-driving operations. This study proposes an improved edge-oriented segmentation-based method to detect the objects from the sensed three-dimensional (3D) point cloud. The improved edge-oriented segmentation-based method consists of three main steps: First, the bounding areas of objects are identified by edge detection and stixel estimation in corresponding two-dimensional (2D) images taken by a stereo camera. Second, 3D sparse point clouds of objects are reconstructed in bounding areas. Finally, the dense point clouds of objects are segmented by matching the 3D sparse point clouds of objects with the whole scene point cloud. After comparison with the existing methods of segmentation, the experimental results demonstrate that the proposed edge-oriented segmentation method improves the precision of 3D point cloud segmentation, and that the objects can be segmented accurately. Meanwhile, the visualization of output data in advanced driving assistance systems (ADAS) can be greatly facilitated due to the decrease in computational time and the decrease in the number of points in the object's point cloud.

The effect of dams on river transport of microplastic pollution.

Dams are known to trap pollutants such as metals and PCBs in the sediment that accumulates within their reservoirs. As more attention is paid to microplastics, an emerging contaminant in waterways worldwide, and how they move along rivers, whether microplastic particles also accumulate behind dams is an important question for informing estimates of global river inputs to oceans. In this study, we measured microplastic concentrations above, below, and within the reservoirs of six dams near Ithaca, NY USA. Samples were processed following the wet peroxide oxidation method and visual counting, followed by Raman Spectroscopy validation. We found that microplastic concentrations in sediment within reservoirs was significantly higher than in sediment above the dams (p = 0.005), and in water samples, concentrations within reservoirs was significantly lower (p = 0.02). Plastic fibers were the dominant plastic type, but in within-reservoir sediment samples, less abundant plastic types such as plastic fragments were found in higher proportions. These results show that the sediment collecting behind dams is one sink for microplastics in river systems at long timescales, indicating that accounting for dams may be important when modeling global riverine microplastic transport.

[Nitrite Accumulation Characteristics of Partial Denitrification in Different Sludge Sources Using Sodium Acetate as Carbon Source].

In order to explore the characteristics of nitrite accumulation during the operational period of partial denitrification in different sludge sources using sodium acetate as a carbon source, No.1 SBR and No.2 SBR were used to inoculate with surplus sludge taken separately from a secondary sedimentation tank of a sewage treatment plant and simultaneous nitrification and denitrifying phosphorus removal system. By reasonably controlling the initial nitrate concentration and anoxic time, partial denitrification was realized. The carbon and nitrogen removal characteristics under different initial COD and NO3--N concentrations were investigated. The results showed that, using sodium acetate as the carbon source, the partial denitrification process in No.1 SBR and No.2 SBR sludge successfully began in 21 d and 20 d, respectively. The accumulation of NO2--N and nitrite accumulation rate (NAR) in reactors were maintained at high levels (12.61 mg·L-1, 79.76% and 13.85 mg·L-1, 87.60%, respectively). When the initial NO3--N concentration of No.2 SBR was 20 mg·L-1 and the initial COD concentration increased from 60 mg·L-1 to 140 mg·L-1, the operation time for achieving the highest NO2--N accumulation in the system was shortened from 160 min to 6 min. The NO3--N ratio of the denitrification rate (in VSS) increased from 3.84 mg·(g·h)-1 to 7.35 mg·(g·h)-1. Increased initial COD concentration was beneficial to the accumulation of NO2--N during partial denitrification. When the initial COD concentration of No.2 SBR was 100 mg·L-1 and the initial NO3--N concentration increased from 20 mg·L-1 to 30 mg·L-1, NAR was maintained above 90% and up to 100% (the initial NO3--N concentration was 25 mg·L-1). When the initial NO3--N concentration was ≥ 35 mg·L-1, insufficient COD caused NO3--N to be completely reduced to NO2--N. Under different initial COD concentrations (80, 100, or 120 mg·L-1) and different initial NO3--N concentrations (20, 25, 30, or 40 mg·L-1), the nitrogen and carbon removal and partial denitrification performance of the No.2 SBR was better than that of No.1 SBR.

How tapeworm infection and consumption of a Cd and Zn hyperaccumulating plant may affect Cu, Fe, and Mn concentrations in an animal-a plant consumer and tapeworm host.

This study evaluated the effects of a hyperaccumulator plant (Arabidopsis halleri), containing surplus of cadmium (Cd) and zinc (Zn) and being an admixture to the rat feed, on concentrations of copper (Cu), iron (Fe), and manganese (Mn) in the tissues of experimental rats infected/uninfected with the tapeworm (Hymenolepis diminuta). Male Wistar rats were divided into three groups (00, P0, and PT); the P0 and PT animals were fed a standard mixture for rats (ST-1) supplemented with the plant A. halleri at a weekly Zn and Cd dosage of 123 and 1 mg, respectively. Moreover, rats from the group PT were infected with the tapeworm. The group 00 served as control animals fed only ST-1 having no tapeworm infection. Rats were euthanized after 6 weeks, and Cu, Fe, and Mn levels were determined in rat and tapeworm tissues. The results indicated that both the consumption of hyperaccumulator plant and/or presence of tapeworms did have significant effect on Cu, Fe, and Mn concentrations in the host tissues. Concentrations of all the elements were higher in the rat liver and partially kidneys than in the tapeworms, and the concentrations of Cu, Fe, and Mn were affected by the consumption of Cd/Zn hyperaccumulator plants. Particularly, Fe concentrations in all rat tissues were significantly increased by consumption of A. halleri while decreased by the presence of tapeworms. Overall, the consumption of a Cd/Zn hyperaccumulator plant and tapeworm infection cause an imbalance in Cu, Fe, and Mn concentrations in the tissues of a consumer (experimental rats).