Low-light Environments Research Articles

Do submerged macrophyte species influence crustacean zooplankton functional group richness and their resource use efficiency in the low-light environment?

During the high grazing of epiphytic zooplankton in submerged macrophyte beds, the changes in crustacean zooplankton functional groups are crucial for stabilizing a clear water state in shallow lakes. However, submerged macrophytes often experience low-light stress due to many ecological processes. It is unclear whether submerged macrophytes alter the zooplankton functional group and their resource use efficiency in the low-light environment. We conducted two mesocosm experiments involving the treatments of low-light and submerged macrophyte species (Vallisneria natans and Potamogeton maackianus). The results show that abiotic factors (e.g., light) were the most important variables in explaining the change in the zooplankton community. Specifically, zooplankton functional group (i.e., pelagic species, plant-associated species, and substrate scrapers) richness and zooplankton species diversity decreased with the decreasing light intensity, especially for low substrate scraper abundance. In addition, structural equation models showed that low-light stress reduced zooplankton resource use efficiency by reducing zooplankton functional group richness and species diversity. Compared to species diversity, zooplankton functional group richness had a greater influence on their resource use efficiency (Zp/Chl-a) in the low-light environment. Our results suggest that the low-light stress reduced zooplankton resource use efficiency by changing their functional group richness. Moreover, the abundance of substrate scrapers shaken from V. natans was higher than that from P. maackianus. Therefore, submerged macrophyte species influence crustacean zooplankton functional group richness and their resource use efficiency in the low-light environment. Selecting appropriate aquatic plant species to assure the high diversity of zooplankton should be considered when conducting lake restoration using submerged macrophytes.

ESVIO: Event-Based Stereo Visual Inertial Odometry

Event cameras that asynchronously output low-latency event streams provide great opportunities for state estimation under challenging situations. Despite event-based visual odometry having been extensively studied in recent years, most of them are based on the monocular, while few research on stereo event vision. In this paper, we present ESVIO, the first event-based stereo visual-inertial odometry, which leverages the complementary advantages of event streams, standard images, and inertial measurements. Our proposed pipeline includes the ESIO (purely event-based) and ESVIO (event with image-aided), which achieves spatial and temporal associations between consecutive stereo event streams. A well-design back-end tightly-coupled fused the multi-sensor measurement to obtain robust state estimation. We validate that both ESIO and ESVIO have superior performance compared with other image-based and event-based baseline methods on public and self-collected datasets. Furthermore, we use our pipeline to perform onboard quadrotor flights under low-light environments. Autonomous driving data sequences and real-world large-scale experiments are also conducted to demonstrate long-term effectiveness. We highlight that this work is a real-time, accurate system that is aimed at robust state estimation under challenging environments.

All-Solution-Processed Reduced Graphene Oxide Flexible Photodetectors for Use in Low-Light Environments

All-solution-processed reduced graphene oxide (rGO)-based flexible photodetectors (PDs) with asymmetric electrode structures of Ag nanowires (NWs) — Cu NWs are demonstrated for stable photodetection in weak-light environments. At first, we aimed to optimize the fabrication parameters of rGO layers for minimum roughness and high uniformity according to the field emission scanning electron microscope (FESEM) images; the average absorbance of optimal rGO layers is ~17.4% from 400 to 1600 nm. Additionally, compared to Ag NWs — Ag NWs and Cu NWs — Cu NWs electrodes, rGO PDs with Ag NWs—Cu NWs asymmetric electrodes showed a higher photocurrent-to-dark-current ratio (PDCR) (~6), which was due to the improved carrier transport with using Ag and Cu as cathodes and anodes. Furthermore, the highest photoresponsivity (5.5 mA/W) and detectivity ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</i> *) (~1×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> cm Hz <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> /W) of rGO PDs with Ag NWs — Cu NWs electrodes have been measured and evaluated at 8 V bias. This work demonstrates the significant potential of all-solution processed rGO-based flexible PDs for high-performance and low-cost photo sensing applications.

Instance Segmentation in the Dark

Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments. In this work, we take a deep look at instance segmentation in the dark and introduce several techniques that substantially boost the low-light inference accuracy. The proposed method is motivated by the observation that noise in low-light images introduces high-frequency disturbances to the feature maps of neural networks, thereby significantly degrading performance. To suppress this “feature noise”, we propose a novel learning method that relies on an adaptive weighted downsampling layer, a smooth-oriented convolutional block, and disturbance suppression learning. These components effectively reduce feature noise during downsampling and convolution operations, enabling the model to learn disturbance-invariant features. Furthermore, we discover that high-bit-depth RAW images can better preserve richer scene information in low-light conditions compared to typical camera sRGB outputs, thus supporting the use of RAW-input algorithms. Our analysis indicates that high bit-depth can be critical for low-light instance segmentation. To mitigate the scarcity of annotated RAW datasets, we leverage a low-light RAW synthetic pipeline to generate realistic low-light data. In addition, to facilitate further research in this direction, we capture a real-world low-light instance segmentation dataset comprising over two thousand paired low/normal-light images with instance-level pixel-wise annotations. Remarkably, without any image preprocessing, we achieve satisfactory performance on instance segmentation in very low light (4% AP higher than state-of-the-art competitors), meanwhile opening new opportunities for future research. Our code and dataset are publicly available to the community ( https://github.com/Linwei-Chen/LIS ).

Neuromorphic high-frequency 3D dancing pose estimation in dynamic environment

Technology-mediated dance experiences, as a medium of entertainment, are a key element in both traditional and virtual reality-based gaming platforms. These platforms predominantly depend on unobtrusive and continuous human pose estimation as a means of capturing input. Current solutions primarily employ RGB or RGB-Depth cameras for dance gaming applications; however, the former is hindered by low-light conditions due to motion blur and reduced sensitivity, while the latter exhibits excessive power consumption, diminished frame rates, and restricted operational distance. Boasting ultra-low latency, energy efficiency, and a wide dynamic range, neuromorphic cameras present a viable solution to surmount these limitations. Here, we introduce YeLan, a neuromorphic camera-driven, three-dimensional, high-frequency human pose estimation (HPE) system capable of withstanding low-light environments and dynamic backgrounds. We have compiled the first-ever neuromorphic camera dance HPE dataset and devised a fully adaptable motion-to-event, physics-conscious simulator. YeLan surpasses baseline models under strenuous conditions and exhibits resilience against varying clothing types, background motion, viewing angles, occlusions, and lighting fluctuations.

Smoke Image Segmentation Algorithm Suitable for Low-Light Scenes

The real-time monitoring and analysis system based on video images has been implemented to detect fire accidents on site. While most segmentation methods can accurately segment smoke areas in bright and clear images, it becomes challenging to obtain high performance due to the low brightness and contrast of low-light smoke images. An image enhancement model cascaded with a semantic segmentation model was proposed to enhance the segmentation effect of low-light smoke images. The modified Cycle-Consistent Generative Adversarial Network (CycleGAN) was used to enhance the low-light images, making smoke features apparent and improving the detection ability of the subsequent segmentation model. The smoke segmentation model was based on Transformers and HRNet, where semantic features at different scales were fused in a dense form. The addition of attention modules of spatial dimension and channel dimension to the feature extraction units established the relationship mappings between pixels and features in the two-dimensional spatial directions, which improved the segmentation ability. Through the Foreground Feature Localization Module (FFLM), the discrimination between foreground and background features was increased, and the ability of the model to distinguish the thinner positions of smoke edges was improved. The enhanced segmentation method achieved a segmentation accuracy of 91.68% on the self-built dataset with synthetic low-light images and an overall detection time of 120.1 ms. This method can successfully meet the fire detection demands in low-light environments at night and lay a foundation for expanding the all-weather application of initial fire detection technology based on image analysis.

Which side are you on? Spider web positioning affects prey capture more than body colour

Colour has been hypothesized to play a crucial role in prey capture for sit-and-wait predators that rely on visual cues to deceive their prey. Prey capture success has been directly linked to colouration in spiders; however, evidence so far focuses mostly on dorsal colouration, excluding ventral patterns that are visible to prey and may be relevant for prey attraction. Here, we explored whether the dorsal and ventral colouration of a colour polymorphic spider is associated with light environment and prey capture success. We quantified the number of prey captured across three dorsal (black, white, and yellow) and two ventral (black and a mosaic yellow/orange pattern) morphs of Gasteracantha cancriformis, considering light intensity on dorsal and ventral sides. We found that spiders capture more prey in low light environments, and that individuals often expose their dorsal colouration towards low light levels. We did not find significant differences in prey capture rate between morphs dorsally or ventrally. These results indicate that how and where spiders position their web can be more important for prey capture than colouration. Alternative hypotheses such as aposematism, camouflage and thermoregulation deserve more attention in future studies that aim to understand the role of colouration in spiders and the factors causing spider colour polymorphisms.

Long‐term capture data uncover shifts in the daily activity patterns of Amazonian birds

Although the duration of biological rhythms varies from milliseconds to decades, daily cycles are especially widespread for individual organisms. These circadian rhythms are synchronized by light–dark cycles, which govern predictable changes in other microclimate variables. Despite the fixed, regular nature of these abiotic cycles at low latitudes, little is known about how different animals structure activity along these gradients. We used 25 years of capture–recapture data and ~ 25 000 unique captures to characterize ‘activity' (time of capture) for 65 species of Amazonian birds in the lowest forest stratum (0–2.5 m). To quantify vertical niche breadths, we also measured foraging heights for these same species in the field. Activity peaked within the first two hours after sunrise for most species (71%), while others peaked 1–3 h later (29%). This difference was associated with an apparent preference for specific vertical strata, as terrestrial and near‐ground birds peaked ~ 1 h after sunrise, while birds from higher strata delayed their first bouts of activity in the lower understory (by an average of 40–100 min). Peak capture times also correlated with long‐term abundance trends, as only species with the earliest capture times showed strong declines, suggesting that associations with mid‐ or late morning microclimates may contribute to resiliency in birds typical of higher strata. Because arboreal species largely avoid the lower understory in the early morning, when terrestrial birds are active, we infer that they are poorly adapted to low light environments. However, with their broad vertical niches and reliance on brighter microhabitats, we suggest that an arboreal lifestyle diminishes specialization and may increase the capacity to cope with a wider range of daily microclimate conditions. We predict that subtle, but important, shifts in daily activity patterns would materialize if other biodiversity‐monitoring programs could analyze similar temporal datasets.

Multi-Sensor Data Fusion and CNN-LSTM Model for Human Activity Recognition System

Human activity recognition (HAR) is becoming increasingly important, especially with the growing number of elderly people living at home. However, most sensors, such as cameras, do not perform well in low-light environments. To address this issue, we designed a HAR system that combines a camera and a millimeter wave radar, taking advantage of each sensor and a fusion algorithm to distinguish between confusing human activities and to improve accuracy in low-light settings. To extract the spatial and temporal features contained in the multisensor fusion data, we designed an improved CNN-LSTM model. In addition, three data fusion algorithms were studied and investigated. Compared to camera data in low-light environments, the fusion data significantly improved the HAR accuracy by at least 26.68%, 19.87%, and 21.92% under the data level fusion algorithm, feature level fusion algorithm, and decision level fusion algorithm, respectively. Moreover, the data level fusion algorithm also resulted in a reduction of the best misclassification rate to 2%~6%. These findings suggest that the proposed system has the potential to enhance the accuracy of HAR in low-light environments and to decrease human activity misclassification rates.

Effects of Shading on the Growth and Carbon Storage of Enhalus acoroides

Light intensity is one of the main factors determining the growth and distribution of seagrasses, but seagrasses differ in their responses to changes in the light environment, resulting in inconsistent adaptation. To investigate the effect of light reduction on Enhalus acoroides (L. f.) Steud., we simulated different light intensities by setting up in situ shade shelters with three light environments: full light (CK), moderate shading (MS) and high shading (HS), and investigated the growth response and adaptation mechanism of E. acoroides to a low-light environment. The results showed that the leaf length and leaf width of E. acoroides decreased in the low-light environment. Plant density, biomass, and chlorophyll content (Chl) decreased significantly with the prolongation of shading. In addition, the sediment carbon content of seagrass beds was significantly reduced in the shading treatments compared to the full-light treatment. After the restoration of light, the chlorophyll content of E. acoroides increased compared to that in the shading period, but its leaf morphology, plant density and biomass did not return to the level of full light treatment. Our study highlights that long-term light reduction leads to a significant reduction in seagrass biomass and its sediment carbon content, which in turn, may reduce the carbon storage capacity of seagrass beds.

Zero-reference deep learning for low-light image enhancement of underground utilities 3D reconstruction

Image-based 3D reconstruction has become one of the most promising as-built construction modeling methods for its high cost-efficiency and outstanding performance. However, the quality performance of image-based 3D reconstruction is very sensitive to the illumination conditions. To date, the image-based 3D reconstruction in low-light environment is mainly optimized by traditional approaches that are time-consuming and manual parameters required. And the supervised deep learning methods request suitable paired image data (low-light images and the paired reference images). Therefore, a Zero-reference Deep learning model for the low-light image Enhancement for underground utilities 3D reconstruction (ZDE3D) is proposed in this paper. ZDE3D improved the 3D reconstruction performance of low-light images by unsupervised loss functions design without paired or unpaired training datasets. Field experiments implemented confirms that the capability of ZDE3D for increasing the quantity of sparse reconstruction point cloud by 13.19% on average and the reconstruction accuracy reached 98.58%.

LL-CSFormer: A Novel Image Denoiser for Intensified CMOS Sensing Images under a Low Light Environment

Intensified complementary metal-oxide semiconductor (ICMOS) sensors can capture images under extremely low-light conditions (≤0.01 lux illumination), but the results exhibit spatially clustered noise that seriously damages the structural information. Existing image-denoising methods mainly focus on simulated noise and real noise from normal CMOS sensors, which can easily mistake the ICMOS noise for the latent image texture. To solve this problem, we propose a low-light cross-scale transformer (LL-CSFormer) that adopts multi-scale and multi-range learning to better distinguish between the noise and signal in ICMOS sensing images. For multi-scale aspects, the proposed LL-CSFormer designs parallel multi-scale streams and ensures information exchange across different scales to maintain high-resolution spatial information and low-resolution contextual information. For multi-range learning, the network contains both convolutions and transformer blocks, which are able to extract noise-wise local features and signal-wise global features. To enable this, we establish a novel ICMOS image dataset of still noisy bursts under different illumination levels. We also designed a two-stream noise-to-noise training strategy for interactive learning and data augmentation. Experiments were conducted on our proposed ICMOS image dataset, and the results demonstrate that our method is able to effectively remove ICMOS image noise compared with other image-denoising methods using objective and subjective metrics.

ULL-SLAM: underwater low-light enhancement for the front-end of visual SLAM

Underwater visual simultaneous localization and mapping (VSLAM), which can provide robot navigation and localization for underwater vehicles, is crucial in underwater exploration. Underwater SLAM is a challenging research topic due to the limitations of underwater vision and error accumulation over long-term operations. When an underwater vehicle goes down, it may inevitably enter a low-light environment. Although artificial light sources could help to some extent, they might also cause non-uniform illumination, which may have an adverse effect on feature point matching. Consequently, the capability of feature point extraction-based visual SLAM systems could only sometimes work. This paper proposes an end-to-end network for SLAM preprocessing in an underwater low-light environment to address this issue. Our model includes a low-light enhancement branch specific with a non-reference loss function, which can achieve low-light image enhancement without requiring paired low-light data. In addition, we design a self-supervised feature point detector and descriptor extraction branch to take advantage of self-supervised learning for feature points and descriptors matching to reduce the re-projection error. Unlike other works, our model does not require pseudo-ground truth. Finally, we design a unique matrix transformation method to improve the feature similarity between two adjacent video frames. Comparative experiments and ablation experiments confirm that the proposed method in this paper could effectively enhance the performance of VSLAM based on feature point extraction in an underwater low-light environment.

Similarity in phytoplankton photophysiology among under-ice, marginal ice, and open water environments of Baffin Bay (Arctic Ocean)

As sea ice is declining rapidly in the Arctic, phytoplankton are being exposed to very different light regimes. Here we investigated how phytoplankton photoacclimate in three different irradiance regimes: under the ice, in the marginal ice zone, and in open water. We sampled from these three regimes in spring–summer 2016 during the Green Edge cruise in Baffin Bay. We also conducted experiments to investigate the impact of short-term surface light exposure on phytoplankton photophysiology, focusing on processes related to photoprotection and photodamage. These experiments were designed to simulate phytoplankton mixing to the surface or sea ice rapidly disappearing. Despite differences in hydrography, nutrient concentrations, light conditions, and phytoplankton biomass in each regime, the phytoplankton community was similar in terms of photophysiological state. Photoprotective pigments (including the xanthophyll cycle) were high in all three regimes sampled. As with the in situ measurements, ice conditions and light history had little impact on how phytoplankton responded to high light exposure, leading us to conclude that phytoplankton are largely prepared for a high light transition, even when originating from low light environments under sea ice.

Carbon fluxes associated with fog in an elfin cloud forest in Anaga (Tenerife, Canary Islands)

The role of fog in the relict laurel forests of the Macaronesia is still not fully understood and it is even more relevant in the Canary Islands, where precipitations are rather scarce and summers are dry. Eddy covariance CO2 exchange (FCO2) measurements in a “brezal de tejo” elfin cloud forest in Tenerife (Canary Islands), over a four-weeks period during the wet season, showed that the vegetation can sustain a carbon assimilation rate in an environment of fog, leading to higher median water use efficiency values (19.5 μmol mg−1 ≤ WUE ≤ 68.3 μmol mg−1) than during no-fog periods (WUE = 1.8 μmol mg−1), being highest under thick fog (WUE = 68.3 μmol mg−1). Median FCO2 values across the day were slightly greater under foggy versus no-fog conditions during the study period. These increments were more prominent under a low light environment, especially in the morning, thus highlighting the important difference between cloud immersion versus high clouds. A greater proportion of light diffusivity due to fog may enhance carbon assimilation, although additional concomitant factors may intervene during thick fog conditions. Also, net carbon assimilation was initiated earlier under foggy conditions. During cloud immersion and depleted light, the maximal CO2 exchange, FCO2_max, diurnal values were higher than those observed when fog was absent. Additionally, FCO2_max remained high (FCO2_max ≈ 13 μmol m−2 s−1) for large vapour pressure deficit conditions (VPD > 0.6 kPa). All this is indicative of a profligate water use strategy of the vegetation, presumably due to low stomatal control. Fog was present during 36.9 % of the time and, as a consequence, the estimated mean daily settling flux of water droplets was 1.186 mg m−2 s−1. For the entire period measured, the cumulative settling flux was 1.30 mm compared to the 97.6 mm of rainfall and the 36.6 mm of throughfall, and the canopy was wet at least 33.3 % of the time. The results suggest that such an anisohydric behaviour of the vegetation permits a continuous growth with maximal water use efficiency, while the frequent presence of fog and wetting of the canopy would minimize the risk of cavitation associated with such non-conservative water use response.

An underwater image enhancement model for domain adaptation

Underwater imaging has been suffering from color imbalance, low contrast, and low-light environment due to strong spectral attenuation of light in the water. Owing to its complex physical imaging mechanism, enhancing the underwater imaging quality based on the deep learning method has been well-developed recently. However, individual studies use different underwater image datasets, leading to low generalization ability in other water conditions. To solve this domain adaptation problem, this paper proposes an underwater image enhancement scheme that combines individually degraded images and publicly available datasets for domain adaptation. Firstly, an underwater dataset fitting model (UDFM) is proposed to merge the individual localized and publicly available degraded datasets into a combined degraded one. Then an underwater image enhancement model (UIEM) is developed base on the combined degraded and open available clear image pairs dataset. The experiment proves that clear images can be recovered by only collecting the degraded images at some specific sea area. Thus, by use of the scheme in this study, the domain adaptation problem could be solved with the increase of underwater images collected at various sea areas. Also, the generalization ability of the underwater image enhancement model is supposed to become more robust. The code is available at https://github.com/fanren5599/UIEM.

HAFNet: Hierarchical Attentive Fusion Network for Multispectral Pedestrian Detection

Multispectral pedestrian detection via visible and thermal image pairs has received widespread attention in recent years. It provides a promising multi-modality solution to address the challenges of pedestrian detection in low-light environments and occlusion situations. Most existing methods directly blend the results of the two modalities or combine the visible and thermal features via a linear interpolation. However, such fusion strategies tend to extract coarser features corresponding to the positions of different modalities, which may lead to degraded detection performance. To mitigate this, this paper proposes a novel and adaptive cross-modality fusion framework, named Hierarchical Attentive Fusion Network (HAFNet), which fully exploits the multispectral attention knowledge to inspire pedestrian detection in the decision-making process. Concretely, we introduce a Hierarchical Content-dependent Attentive Fusion (HCAF) module to extract top-level features as a guide to pixel-wise blending features of two modalities to enhance the quality of the feature representation and a plug-in multi-modality feature alignment (MFA) block to fine-tune the feature alignment of two modalities. Experiments on the challenging KAIST and CVC-14 datasets demonstrate the superior performance of our method with satisfactory speed.

Helical allophycocyanin nanotubes absorb far-red light in a thermophilic cyanobacterium.

To compete in certain low-light environments, some cyanobacteria express a paralog of the light-harvesting phycobiliprotein, allophycocyanin (AP), that strongly absorbs far-red light (FRL). Using cryo-electron microscopy and time-resolved absorption spectroscopy, we reveal the structure-function relationship of this FRL-absorbing AP complex (FRL-AP) that is expressed during acclimation to low light and that likely associates with chlorophyll a-containing photosystem I.FRL-AP assembles as helical nanotubes rather than typical toroids due to alterations of the domain geometry within each subunit. Spectroscopic characterization suggests that FRL-AP nanotubes are somewhat inefficient antenna; however, the enhanced ability to harvest FRL when visible light is severely attenuated represents a beneficial trade-off. The results expand the known diversity of light-harvesting proteins in nature and exemplify how biological plasticity is achieved by balancing resource accessibility with efficiency.

Proper use of light environments for mitigating the effects of COVID-19 and other prospective public health emergency lockdowns on sleep quality and fatigue in adolescents

Coronavirus disease 2019 (COVID-19) remains a public health emergency of international concern, and some countries still implement strict regional lockdowns. Further, the upcoming 2023 Asian Games and World University Games will implement a closed-loop management system. Quarantine can harm mental and physical health, to which adolescents are more vulnerable compared with adults. Previous studies indicated that light can affect our psychology and physiology, and adolescents were exposed to the artificial light environment in the evening during the lockdown. Thus, this study aimed to establish and assess appropriate residential light environments to mitigate the effects of lockdowns on sleep quality and fatigue in adolescents. The participants were 66 adolescents (12.15 ± 2.45 years of age) in a closed-loop management environment, who participated in a 28-day (7-day baseline, 21-day light intervention) randomized controlled trial of a light-emitting diode (LED) light intervention. The adolescents were exposed to different correlated color temperature (CCT) LED light environments (2000 K or 8000 K) for 1 h each evening. The results for self-reported daily sleep quality indicated that the low CCT LED light environment significantly improved sleep quality (p < 0.05), and the blood test results for serum urea and hemoglobin indicated that this environment also significantly reduced fatigue (p < 0.05) and moderately increased performance, compared to the high CCT LED light environment. These findings can serve as a springboard for further research that aims to develop interventions to reduce the effects of public health emergency lockdowns on mental and physical health in adolescents, and provide a reference for participants in the upcoming Asian Games and World University Games.

Formation characteristics of algal-bacteria granular sludge under low-light environment: From sludge characteristics, extracellular polymeric substances to microbial community

In this study, the formation characteristics of algal-bacteria granular sludge (ABGS) under low-light environment (80, 110, and 140 μmol/m2/s) were investigated. The findings revealed that the stronger light intensity favored the improvement of sludge characteristics, nutrient removal performances, and extracellular polymeric substance (EPS) secretion at the growing stage, which were more preferential to facilitate the formation of ABGS. However, after the mature stage, the weaker light intensity ensured more stable operation of the system, as shown by contributing to sludge settlement performance, denitrification, and EPS secretion. According to the results of high-throughput sequencing, the dominant bacterial genus of the mature ABGS cultured under low light intensity were all Zoogloe, while the dominant algal genus was different. For the mature ABGS, the 140 and 80 μmol/m2/s light intensity had the most significant activation effect to the functional genes related to carbohydrate metabolism and amino acid metabolism, respectively.