A systematic review on aquaculture wastewater: Pollutants, impacts, and treatment technology.

Aquaculture is the major way to solve the global food sacrcity. As the global population increases, the demand for aquaculture increases. Fish feed, drugs and chemicals, and metabolic waste or mortalities of aquatic organisms also increase, eventually resulting in the production of a large amount of aquaculture wastewater. These aquaculture discharges contain a variety of pollutants, such as conventional pollutants, organic compounds, heavy metals, and biological contaminants, inducing occupational hazards and risks, food security, the environment pollution. Proper wastewater treatment technologies are required to remove hazardous pollutants for minimizing their impacts on environmental and human health. Recirculating aquaculture systems, some biological and physicochemical methods have been applied to remove some pollutants from the aquaculture wastewater, but their efficiency in removing pollutants still requires to be further improved for achieving zero-waste discharge and ensuring sustainable aquaculture development. Meanwhile, sound regulation and legislation needs to be established for ensuring the normal operation of aquaculture industries and the standard discharge of wastewater. This review aims to provide comprehensive information of aquaculture wastewater for the researchers and promote the healthy development of aquaculture.

Clinical Therapy: HAIC Combined with Tyrosine Kinase Inhibitors and Programmed Cell Death Protein-1 Inhibitors versus HAIC Alone for Unresectable Hepatocellular Carcinoma.

Purpose: The majority of new diagnoses of hepatocellular carcinoma (HCC) still pertain to unresectable cases. Currently, the combination therapy of tyrosine kinase inhibitors (TKIs) and programmed cell death protein-1 (PD-1) inhibitors has become the mainstream treatment. According to multiple clinical guidelines, it is strongly advised to consider local therapy as the primary treatment choice for uHCC. This research was conducted to examine the safety and effectiveness of combining hepatic arterial infusion chemotherapy (HAIC) with TKIs and PD-1 inhibitors for the treatment of uHCC. Methods: Between 2015 and 2020, 208 HCC patients received HAIC alone or HAIC in combination with TKIs and PD-1 inhibitors. The overall survival(OS), and progression-free survival(PFS) and the best treatment response were compared between the two treatment groups. Propensity score matching (PSM)was used to minimize confounding bias. Results: Among the enrolled patients, 116 patients (55.8%) received combination therapy, while 92 patients (44.2%) received HAIC alone. The baseline characteristics were similar between the two groups. After PSM, 82 pairs of well-matched liver cancer patients were selected; the overall response rate in the combination group trended better than that in the HAIC alone group. The hazard ratios (HRs) for OS and PFS of the combination approach compared to the HAIC-alone approach were 0.47 (95% CI, 0.322-0.687; p<0.001) and 0.58 (95% CI, 0.397-0.848; p=0.005), respectively. Conclusion: For uHCC patients, combination therapy can provide better OS and PFS compared to HAIC alone.

Achieving High-Performance Polymer-Wrapper-Free Aligned Carbon Nanotube Field-Effect Transistors Through Degradable Polymer Wrapping and Efficient Removal Techniques.

Semiconducting carbon nanotubes (s-CNTs) have emerged as a promising alternative to traditional silicon for ultrascaled field-effect transistors (FETs), owing to their exceptional properties. Aligned s-CNTs (A-CNTs) are particularly favored for practical applications due to their ability to provide higher driving current and lower contact resistance compared with individual s-CNTs or random networks. Achieving high-semiconducting-purity A-CNTs typically involves conjugated polymer wrapping for selective separation of s-CNTs, followed by self-assembly techniques. However, the presence of the polymer wrapper on A-CNTs can adversely impact electrical contact, gating efficiency, carrier transport, and device-to-device variations, necessitating its complete removal. While various methods have been explored for polymer removal, accurately characterizing the extent of removal remains a challenge. Traditional techniques such as absorption spectroscopy and X-ray photoelectron spectroscopy (XPS) may not accurately depict the remaining polymer content on A-CNTs due to their inherent detection limits. Consequently, the performance of FETs based on pure polymer-wrapper-free A-CNTs is unclear. In this study, we present an approach for preparing high-semiconducting-purity and polymer-wrapper-free A-CNTs using poly[(9,9-dioctylfluorenyl-2,7-dinitrilomethine)-(9,9-dioctylfluorenyl-2,7-dimethine)] (PFO-N-PFO), a degradable polymer, in conjunction with a modified dimension-limited self-alignment process (m-DLSA). Comprehensive transmission electron microscopy (TEM) characterizations, complemented by absorption and XPS characterizations, provide robust evidence of the successful near-complete removal of the polymer wrapper via a cleaning procedure involving acidic degradation, hot solvent rinsing, and vacuum annealing. Furthermore, top-gated FETs based on these high-semiconducting-purity and polymer-wrapper-free A-CNTs exhibit good performance metrics, including an on-current (Ion) of 2.2 mA/µm, peak transconductance (gm) of 1.1 mS/µm, low contact resistance (Rc) of 191 Ω·µm, and negligible hysteresis, representing a significant advancement in the CNT-based FET technology.

Genetic diversity and population structure of an insect-pollinated and bird-dispersed dioecious tree Magnolia kwangsiensis in a fragmented karst forest landscape.

This study combined population genetics and parentage analysis to obtain foundational data for the conservation of Magnolia kwangsiensis. M. kwangsiensis is a Class I tree species that occurs in two disjunct regions in a biodiversity hotspot in southwest China. We assessed the genetic diversity and structure of this species across its distribution range to support its conservation management. Genetic diversity and population structure of 529 individuals sampled from 14 populations were investigated using seven nuclear simple sequence repeat (nSSR) markers and three chloroplast DNA (cpDNA) fragments. Parentage analysis was used to evaluate the pollen and seed dispersal distances. The nSSR marker analysis revealed a high genetic diversity in M. kwangsiensis, with an average observed (Ho) and expected heterozygosities (He) of 0.726 and 0.687, respectively. The mean and maximum pollen and seed dispersal distances were 66.4 and 95.7 m and 535.4 and 553.8 m, respectively. Our data revealed two distinct genetic groups, consistent with the disjunct geographical distribution of the M. kwangsiensis populations. Both pollen and seed dispersal movements help maintain genetic connectivity among M. kwangsiensis populations, contributing to high levels of genetic diversity. Both genetically differentiated groups corresponding to the two disjunct regions should be recognized as separate conservation units.

iJAZ: the next breakthrough for engineering pest-resistance in plants?

Although transgenic Bacillus thuringiensis (Bt) crops have brought various ecological and socioeconomic benefits, there is evidence suggesting that pests will eventually develop resistance to Bt crops. Thus, additional genes are urgently needed to engineer pest resistance in plants. A recent study by Mo et al. indicates that iJAZ maybe the next breakthrough for engineering pest resistance in plants.

Contrasting demographic history and mutational load in three threatened whitebark pines (Pinus subsect. Gerardianae): implications for conservation.

Demographic history and mutational load are of paramount importance for the adaptation of the endangered species. However, the effects of population evolutionary history and genetic load on the adaptive potential in endangered conifers remain unclear. Here, using population transcriptome sequencing, whole chloroplast genomes and mitochondrial DNA markers, combined with niche analysis, we determined the demographic history and mutational load for three threatened whitebark pines having different endangered statuses, Pinus bungeana, P. gerardiana and P. squamata. Demographic inference indicated that severe bottlenecks occurred in all three pines at different times, coinciding with periods of major climate and geological changes; in contrast, while P. bungeana experienced a recent population expansion, P. gerardiana and P. squamata maintained small population sizes after bottlenecking. Abundant homozygous-derived variants accumulated in the three pines, particularly in P. squamata, while the species with most heterozygous variants was P. gerardiana. Abundant moderately and few highly deleterious variants accumulated in the pine species that have experienced the most severe demographic bottlenecks (P. gerardiana and P. squamata), most likely because of purging effects. Finally, niche modeling showed that the distribution of P. bungeana might experience a significant expansion in the future, and the species' identified genetic clusters are also supported by differences in the ecological niche. The integration of genomic, demographic and niche data has allowed us to prove that the three threatened pines have contrasting patterns of demographic history and mutational load, which may have important implications in their adaptive potential and thus are also key for informing conservation planning.

Mass Production of Carbon Nanotube Transistor Biosensors for Point-of-Care Tests.

Low-dimensional semiconductor-based field-effect transistor (FET) biosensors are promising for label-free detection of biotargets while facing challenges in mass fabrication of devices and reliable reading of small signals. Here, we construct a reliable technology for mass production of semiconducting carbon nanotube (CNT) film and FET biosensors. High-uniformity randomly oriented CNT films were prepared through an improved immersion coating technique, and then, CNT FETs were fabricated with coefficient of performance variations within 6% on 4-in. wafers (within 9% interwafer) based on an industrial standard-level process. The CNT FET-based ion sensors demonstrated threshold voltage standard deviations within 5.1 mV at each ion concentration, enabling direct reading of the concentration information based on the drain current. By integrating bioprobes, we achieved detection of biosignals as low as 100 aM through a plug-and-play portable detection system. The reliable technology will contribute to commercial applications of CNT FET biosensors, especially in point-of-care tests.

Protein Corona-Directed Cellular Recognition and Uptake of Polyethylene Nanoplastics by Macrophages.

The widespread use of plastic products in daily life has raised concerns about the health hazards associated with nanoplastics (NPs). When exposed, NPs are likely to infiltrate the bloodstream, interact with plasma proteins, and trigger macrophage recognition and clearance. In this study, we focused on establishing a correlation between the unique protein coronal signatures of high-density (HDPE) and low-density (LDPE) polyethylene (PE) NPs with their ultimate impact on macrophage recognition and cytotoxicity. We observed that low-density and high-density lipoprotein receptors (LDLR and SR-B1), facilitated by apolipoproteins, played an essential role in PE-NP recognition. Consequently, PE-NPs activated the caspase-3/GSDME pathway and ultimately led to pyroptosis. Advanced imaging techniques, including label-free scattered light confocal imaging and cryo-soft X-ray transmission microscopy with 3D-tomographic reconstruction (nano-CT), provided powerful insights into visualizing NPs-cell interactions. These findings underscore the potential risks of NPs to macrophages and introduce analytical methods for studying the behavior of NPs in biological systems.

IL-1ß mediates Candida tropicalis-induced immunosuppressive function of MDSCs to foster colorectal cancer.

BACKGROUND: There is increasing evidence that gut fungi dysbiosis plays a crucial role in the development and progression of colorectal cancer (CRC). It has been reported that gut fungi exacerbate the severity of CRC by regulating tumor immunity. Our previous studies have shown that the opportunistic pathogenic fungal pathogen, Candida tropicalis (C. tropicalis) promotes CRC progression by enhancing the immunosuppressive function of MDSCs and activating the NLRP3 inflammasome of MDSCs. However, the relationship between IL-1ß produced by NLRP3 inflammasome activation and the immunosuppressive function of MDSCs enhanced by C. tropicalis in CRC remains unclear. METHODS: The TCGA database was used to analyze the relationship between IL-1ß and genes related to immunosuppressive function of MDSCs in human CRC. The expression of IL-1ß in human CRC tissues was detected by immunofluorescence staining. The proteomic analysis was performed on the culture supernatant of C. tropicalis-stimulated MDSCs. The experiments of supplementing and blocking IL-1ß as well as inhibiting the NLRP3 inflammasome activation were conducted. A mouse colon cancer xenograft model was established by using MC38 colon cancer cell line. RESULTS: Analysis of CRC clinical samples showed that the high expression of IL-1ß was closely related to the immunosuppressive function of tumor-infiltrated MDSCs. The results of in vitro experiments revealed that IL-1ß was the most secreted cytokine of MDSCs stimulated by C. tropicalis. In vitro supplementation of IL-1ß further enhanced the immunosuppressive function of C. tropicalis-stimulated MDSCs and NLRP3-IL-1ß axis mediated the immunosuppressive function of MDSCs enhanced by C. tropicalis. Finally, blockade of IL-1ß secreted by MDSCs augmented antitumor immunity and mitigated C. tropicalis-associated colon cancer. CONCLUSIONS: C. tropicalis promotes excessive secretion of IL-1ß from MDSCs via the NLRP3 inflammasome. IL-1ß further enhances the immunosuppressive function of MDSCs to inhibit antitumor immunity, thus promoting the progression of CRC. Therefore, targeting IL-1ß secreted by MDSCs may be a potential immunotherapeutic strategy for the treatment of CRC.

Nonideality in Arrayed Carbon Nanotube Field Effect Transistors Revealed by High-Resolution Transmission Electron Microscopy.

High density and high semiconducting-purity single-walled carbon nanotube array (A-CNT) have recently been demonstrated as promising candidates for high-performance nanoelectronics. Knowledge of the structures and arrangement of CNTs within the arrays and their interfaces to neighboring CNTs, metal contacts, and dielectrics, as the key components of an A-CNT field effect transistor (FET), is essential for device mechanistic understanding and further optimization, particularly considering that the current technologies for the fabrication of A-CNT wafers are mainly laboratory-level solution-based processes. Here, we conduct a systematic investigation into the microstructures of A-CNT FETs mainly via cross-sectional high-resolution transmission electron microscopy and tentatively establish a framework consisting of up to 11 parameters which can be used for structure-side quality evaluation of the A-CNT FETs. The parameter ensemble includes the diameter, length (or terminal), and density distribution of CNTs, radial deformation of CNTs, array alignment defects, surface crystallography facets of contact metal, thickness distribution of high-k dielectrics (HfO2), and the contact ratios for the CNT-CNT, CNT-metal, CNT-dielectric, and CNT-substrate interfaces. Enriched array alignment defects, i.e., bundle, stacking, misorientation, and voids, are observed with a total ratio sometimes up to ∼90% in pristine A-CNTs and even up to ∼95% after the device fabrication process. Thus, they are suggested as the prevalent performance-limiting factors for A-CNT FETs. Complex interfacial structures are observed at the CNT-CNT, CNT-metal contact, and CNT-high-k dielectric interfaces, making the local environment and the property of each component CNT involved in an A-CNT FET distinct from others in terms of the diameters, radial deformation, and interactions with the local surroundings (mainly through van der Waals interactions). The present study suggests further improvements on the fabrication technology of A-CNT wafers and devices and mechanistic investigations into the impacts of complex array alignment defects and interface structures on the electrical performance of A-CNT FETs as well.

IDRWalker: A Random Walk Based Tool for Generating Intrinsically Disordered Regions in Large Protein Complexes.

Intrinsically disordered regions (IDRs), which may be functionally important, are common in proteins. However, the structures of IDRs are often missing due to their highly dynamic nature. In the study of IDRs, integrative modeling combining computational simulations and experimental data is a common approach, for which initial structures of the IDRs need to be built. However, applying this method to large protein complexes is challenging because existing structure generation tools are sometimes unsuitable for IDRs in large systems. To facilitate convenient and rapid structure generation of IDRs in large protein complexes, we developed a computational tool named IDRWalker based on self-avoiding random walks. Three protein complexes were used to illustrate the efficiency of the tool, and it was found that IDRs in more than 800 chains of the nuclear pore complex could be generated in minutes. These structures of large protein complexes with added IDRs can be further used to run computational simulations for integrative modeling.

Chemical constituents, pharmacological activities and quality evaluation methods of genus Hippocampus: A comprehensive review.

The genus Hippocampus is a multi-origin animal species with high medicinal and healthcare values. About 57 species of Hippocampus spread worldwide, of which about 14 species can be used as medicine, showing anti-oxidation, anti-inflammation, anti-depressant, anti-hypertension, anti-prostatic hyperplasia, antivirus, anti-apoptotic, antifatigue, and so on. And those pharmacological effects are mainly related to their active ingredients, including amino acids, abundant proteins (peptides and oligopeptides), fatty acids, nucleosides, steroids, and other small molecular compounds. The main means of authentication of Hippocampus species are morphological identification, microscopic identification, thin layer chromatography method, fingerprint method and genomics method. This review will provide useful insight for exploration, further study and precise medication of Hippocampus in the future.

Interface States in Gate Stack of Carbon Nanotube Array Transistors.

A deep understanding of the interface states in metal-oxide-semiconductor (MOS) structures is the premise of improving the gate stack quality, which sets the foundation for building field-effect transistors (FETs) with high performance and high reliability. Although MOSFETs built on aligned semiconducting carbon nanotube (A-CNT) arrays have been considered ideal energy-efficient successors to commercial silicon (Si) transistors, research on the interface states of A-CNT MOS devices, let alone their optimization, is lacking. Here, we fabricate MOS capacitors based on an A-CNT array with a well-designed layout and accurately measure the capacitance-voltage and conductance-voltage (C-V and G-V) data. Then, the gate electrostatics and the physical origins of interface states are systematically analyzed and revealed. In particular, targeted improvement of gate dielectric growth in the A-CNT MOS device contributes to suppressing the interface state density (Dit) to 6.1 × 1011 cm-2 eV-1, which is a record for CNT- or low-dimensional semiconductors-based MOSFETs, boosting a record transconductance (gm) of 2.42 mS/µm and an on-off ratio of 105. Further decreasing Dit below 1 × 1011 cm-2 eV-1 is necessary for A-CNT MOSFETs to achieve the expected high energy efficiency.

Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI.

Meniscal injury is a common cause of knee joint pain and a precursor to knee osteoarthritis (KOA). The purpose of this study is to develop an automatic pipeline for meniscal injury classification and localization using fully and weakly supervised networks based on MRI images. In this retrospective study, data were from the osteoarthritis initiative (OAI). The MR images were reconstructed using a sagittal intermediate-weighted fat-suppressed turbo spin-echo sequence. (1) We used 130 knees from the OAI to develop the LGSA-UNet model which fuses the features of adjacent slices and adjusts the blocks in Siam to enable the central slice to obtain rich contextual information. (2) One thousand seven hundred and fifty-six knees from the OAI were included to establish segmentation and classification models. The segmentation model achieved a DICE coefficient ranging from 0.84 to 0.93. The AUC values ranged from 0.85 to 0.95 in the binary models. The accuracy for the three types of menisci (normal, tear, and maceration) ranged from 0.60 to 0.88. Furthermore, 206 knees from the orthopedic hospital were used as an external validation data set to evaluate the performance of the model. The segmentation and classification models still performed well on the external validation set. To compare the diagnostic performances between the deep learning (DL) models and radiologists, the external validation sets were sent to two radiologists. The binary classification model outperformed the diagnostic performance of the junior radiologist (0.82-0.87 versus 0.74-0.88). This study highlights the potential of DL in knee meniscus segmentation and injury classification which can help improve diagnostic efficiency.

3D-printed Mg-incorporated PCL-based scaffolds improves rotator cuff tendon-bone healing through regulating macrophage polarization.

Introduction: Rotator cuff tear (RCT) is a common shoulder injury impacting mobility and quality of life, while traditional surgeries often result in poor healing. Tissue engineering offers a promising solution, with poly (ε-caprolactone) (PCL) being favored due to its slow degradation, biocompatibility, and non-toxicity. However, PCL lacks sufficient compression resistance. Incorporating Mg, which promotes bone growth and has antibacterial effects, could enhance RCT repair. Methods: The Mg-incorporated PCL-based scaffolds were fabricated using a 3D printing technique. The scaffolds were incorporated with different percentages of Mg (0%, 5%, 10%, 15%, and 20%). The osteogenic activities and anti-inflammatory properties of the scaffolds were evaluated in vitro using human osteoblasts and macrophages. The tissue ingrowth and biocompatibility of the scaffolds were assessed in vivo using a rat model of RCT repair. The ability of the scaffolds to enhance macrophage polarization towards the M2 subtype and inhibit inflammation signaling activation was also investigated. Results: It was found that when incorporated with 10% Mg, PCL-based scaffolds exhibited the optimal bone repairing ability in vitro and in vivo. The in vitro experiments indicated that the successfully constructed 10 Mg/PCL scaffolds enhance osteogenic activities and anti-inflammatory properties. Besides, the in vivo studies demonstrated that 10 Mg/PCL scaffolds promoted tissue ingrowth and enhanced biocompatibility compared to the control PCL scaffolds. Furthermore, the 10 Mg/PCL scaffolds enhanced the macrophages' ability to polarize towards the M2 subtype and inhibited inflammation signaling activation. Discussion: These findings suggest that 3D-printed Mg-incorporated PCL scaffolds have the potential to improve RCT by enhancing osteogenesis, reducing inflammation, and promoting macrophage polarization. The incorporation of 10% Mg into PCL-based scaffolds provided the optimal combination of properties for RCT repair augmentation. This study highlights the potential of tissue engineering approaches in improving the outcomes of RCT repair and provides a foundation for future clinical applications.

Leverage Effective Deep Learning Searching Method for Forensic Age Estimation.

Dental age estimation is extensively employed in forensic medicine practice. However, the accuracy of conventional methods fails to satisfy the need for precision, particularly when estimating the age of adults. Herein, we propose an approach for age estimation utilizing orthopantomograms (OPGs). We propose a new dental dataset comprising OPGs of 27,957 individuals (16,383 females and 11,574 males), covering an age range from newborn to 93 years. The age annotations were meticulously verified using ID card details. Considering the distinct nature of dental data, we analyzed various neural network components to accurately estimate age, such as optimal network depth, convolution kernel size, multi-branch architecture, and early layer feature reuse. Building upon the exploration of distinctive characteristics, we further employed the widely recognized method to identify models for dental age prediction. Consequently, we discovered two sets of models: one exhibiting superior performance, and the other being lightweight. The proposed approaches, namely AGENet and AGE-SPOS, demonstrated remarkable superiority and effectiveness in our experimental results. The proposed models, AGENet and AGE-SPOS, showed exceptional effectiveness in our experiments. AGENet outperformed other CNN models significantly by achieving outstanding results. Compared to Inception-v4, with the mean absolute error (MAE) of 1.70 and 20.46 B FLOPs, our AGENet reduced the FLOPs by 2.7×. The lightweight model, AGE-SPOS, achieved an MAE of 1.80 years with only 0.95 B FLOPs, surpassing MobileNetV2 by 0.18 years while utilizing fewer computational operations. In summary, we employed an effective DNN searching method for forensic age estimation, and our methodology and findings hold significant implications for age estimation with oral imaging.

The architecture of silk-secreting organs during the final larval stage of silkworms revealed by single-nucleus and spatial transcriptomics.

Natural silks are renewable proteins with impressive mechanical properties and biocompatibility that are useful in various fields. However, the cellular and spatial organization of silk-secreting organs remains unclear. Here, we combined single-nucleus and spatially resolved transcriptomics to systematically map the cellular and spatial composition of the silk glands (SGs) of mulberry silkworms late in larval development. This approach allowed us to profile SG cell types and cell state dynamics and identify regulatory networks and cell-cell communication related to efficient silk protein synthesis; key markers were validated via transgenic approaches. Notably, we demonstrated the indispensable role of the ecdysone receptor (ultraspiracle) in regulating endoreplication in SG cells. Our atlas presents the results of spatiotemporal analysis of silk-secreting organ architecture late in larval development; this atlas provides a valuable reference for elucidating the mechanism of efficient silk protein synthesis and developing sustainable products made from natural silk.

Association between Copper Exposure and Cognitive Function: A Cross-Sectional Study in a County, Guangxi, China.

There has been growing attention to the impact of copper exposure on cognitive function; however, current research on the specific information regarding urinary copper and cognitive function is limited, particularly detailed analyses in the Chinese adult population. This study aimed to explore the association between copper exposure and cognitive function in a cross-sectional design. A total of 2617 participants in a county, Guangxi Zhuang Autonomous Region (Guangxi), China, were included. The mini-mental state examination (MMSE) was used to assess cognitive function, and inductively coupled plasma mass spectrometry was used to measure urinary metal levels. Spearman's rank correlation was used to analyze the correlation between urinary copper levels and various cognitive function assessment indices. After adjusting for potential confounders, binary logistic regression was used to explore the association between urinary copper levels and the risk of cognitive impairment (CI) as revealed by MMSE, and restricted cubic spline regression was further used to explore the dose-response relationship. The results showed a negative correlation between urinary copper levels and orientation, attention and calculation, memory, language ability, and MMSE total scores (P < 0.05). Compared with the low copper exposure group, the high exposure group showed a 58.5% increased risk of CI (OR = 1.585, 95%CI: 1.125 to 2.235, P = 0.008). A significant linear dose-response relationship was observed between urinary copper levels and the risk of CI (P overall = 0.045, P nonlinearity = 0.081). Our findings suggest that higher copper exposure may be associated with CI in the population of a county, Guangxi, China.

A Multi-Scale-Enhanced YOLO-V5 Model for Detecting Small Objects in Remote Sensing Image Information.

As a typical component of remote sensing signals, remote sensing image (RSI) information plays a strong role in showing macro, dynamic and accurate information on the earth's surface and environment, which is critical to many application fields. One of the core technologies is the object detection (OD) of RSI signals (RSISs). The majority of existing OD algorithms only consider medium and large objects, regardless of small-object detection, resulting in an unsatisfactory performance in detection precision and the miss rate of small objects. To boost the overall OD performance of RSISs, an improved detection framework, I-YOLO-V5, was proposed for OD in high-altitude RSISs. Firstly, the idea of a residual network is employed to construct a new residual unit to achieve the purpose of improving the network feature extraction. Then, to avoid the gradient fading of the network, densely connected networks are integrated into the structure of the algorithm. Meanwhile, a fourth detection layer is employed in the algorithm structure in order to reduce the deficiency of small-object detection in RSISs in complex environments, and its effectiveness is verified. The experimental results confirm that, compared with existing advanced OD algorithms, the average accuracy of the proposed I-YOLO-V5 is improved by 15.4%, and the miss rate is reduced by 46.8% on the RSOD dataset.