SiPM joint likelihood reception for mobile LED-optical communication under pointing errors.

Light-emitting diode (LED)-optical communication is a novel spectrum communication with wide field of view (FOV), light weight, and long-distance free-space capabilities. Due to atmospheric turbulence attenuation and pointing errors caused by long-distance communication, this Letter proposes a multi-pixel channel joint maximum likelihood (JML) reception method using a highly sensitive silicon photomultiplier (SiPM). To evaluate the performance of the SiPM under mobile terminal jittering communication, we analyze the effect of optical transmitting power, pointing errors, and signal-to-noise ratio (SNR) gain on optical communication by comparing JML with signal channel using the maximum likelihood (ML) algorithm. Both simulation analysis and experimental results demonstrate that the proposed JML algorithm to process signals received from SiPM multi-pixel channels can effectively mitigate the impact of pointing errors on the bit error rate (BER) of optical communications by two orders of magnitude at large jitter radians and SNR.

Mobile recognition and positioning for multiple visible light communication cells using a convolutional neural network: publisher's note.

This publisher's note contains a correction to Opt. Lett.48, 6468 (2024)10.1364/OL.503007.

Rare case of septic shock combined with meningitis caused by Pasteurella multocida without a history of cat and dog bites.

BACKGROUND: Pasteurella multocida is a zoonotic pathogen that mainly causes local skin and soft tissue infections in the human body through cat and dog bites. It rarely causes bacteraemia (or sepsis) and meningitis. We reported a case of septic shock and meningitis caused by P. multocida in a patient without a history of cat and dog bites. CASE PRESENTATION: An 84-year-old male patient was urgently sent to the emergency department after he was found with unclear consciousness for 8 h, accompanied by limb tremors and urinary incontinence. In the subsequent examination, P. multocida was detected in the blood culture and wound secretion samples of the patient. However, it was not detected in the cerebrospinal fluid culture, but its DNA sequence was detected. Therefore, the patient was clearly diagnosed with septic shock and meningitis caused by P. multocida. The patient had no history of cat or dog contact or bite. The patient was subsequently treated with a combination of penicillin G, doxycycline, and ceftriaxone, and he was discharged after 35 days of hospitalisation. CONCLUSION: This report presented a rare case of septic shock and meningitis caused by P. multocida, which was not related to a cat or dog bite. Clinical doctors should consider P. multocida as a possible cause of sepsis or meningitis and should be aware of its potential seriousness even in the absence of animal bites.

Equivalent analysis model of a GaN LED used as a receiver.

In general, visible light communication (VLC) uses LEDs as transmitters. However, LEDs can serve as receivers to construct a simple duplex VLC system that uses only two LEDs instead of one LED and one photo-diode (PD). There is a lack of effective equivalent analysis models for characterizing and evaluating the inherent behavioral characteristics of LEDs used as receivers. This paper presents an equivalent analysis model for GaN LEDs as receivers. First, based on the proposed receiving equivalent circuit model, a third-order signal transmission mathematical analysis model is established, revealing the transmission relationship between the photocurrent and output voltage. Further research is conducted on the impact of parameter changes on the bandwidth, and the model can be simplified into a first-order low-pass mathematical analysis model under specific conditions, providing theoretical support for improving the bandwidth of LED receiving applications. The experimental results also confirm the theoretical predictions. This research result holds significant importance for revealing the intrinsic mechanisms and the improved optical communication performance of LEDs for effective reception.

Bionic Jaw-Like Micro One-Way Valve for Rapid and Long-Distance Water Droplet Unidirectional Spreading.

A surface with asymmetric microstructures for self-driven directional spreading of liquid has attracted keen interest from researchers in recent years for its great application prospects. Inspired by the jaws of tiny insects, such as ants, a surface textured with novel jaw-like microstructures as micro one-way valves is reported. These microstructures are almost two-dimensional, thus being simple and easy to fabricate. Whereas surfaces with such jaw-like micro one-way valves exhibit amazing rapid and long-distance water droplet unidirectional spreading behaviors. The maximum forward-backward distance ratio of water droplets on surfaces with the optimized microstructures is about 14.5, almost twice those of previous research. The capillary attraction at the location of the mouth of the jaws and the pinning effect brought by the sharp edge of the jaws for the precursor film are analyzed and deduced as the main mechanisms. The findings open a promising avenue for 2D asymmetric microstructure design and effective self-driven liquid unidirectional spreading.

Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn's disease: a multi-omics Mendelian randomization study.

BACKGROUND: Oxidative stress (OS) is a key pathophysiological mechanism in Crohn's disease (CD). OS-related genes can be affected by environmental factors, intestinal inflammation, gut microbiota, and epigenetic changes. However, the role of OS as a potential CD etiological factor or triggering factor is unknown, as differentially expressed OS genes in CD can be either a cause or a subsequent change of intestinal inflammation. Herein, we used a multi-omics summary data-based Mendelian randomization (SMR) approach to identify putative causal effects and underlying mechanisms of OS genes in CD. METHODS: OS-related genes were extracted from the GeneCards database. Intestinal transcriptome datasets were collected from the Gene Expression Omnibus (GEO) database and meta-analyzed to identify differentially expressed genes (DEGs) related to OS in CD. Integration analyses of the largest CD genome-wide association study (GWAS) summaries with expression quantitative trait loci (eQTLs) and DNA methylation QTLs (mQTLs) from the blood were performed using SMR methods to prioritize putative blood OS genes and their regulatory elements associated with CD risk. Up-to-date intestinal eQTLs and fecal microbial QTLs (mbQTLs) were integrated to uncover potential interactions between host OS gene expression and gut microbiota through SMR and colocalization analysis. Two additional Mendelian randomization (MR) methods were used as sensitivity analyses. Putative results were validated in an independent multi-omics cohort from the First Affiliated Hospital of Sun Yat-sen University (FAH-SYS). RESULTS: A meta-analysis from six datasets identified 438 OS-related DEGs enriched in intestinal enterocytes in CD from 817 OS-related genes. Five genes from blood tissue were prioritized as candidate CD-causal genes using three-step SMR methods: BAD, SHC1, STAT3, MUC1, and GPX3. Furthermore, SMR analysis also identified five putative intestinal genes, three of which were involved in gene-microbiota interactions through colocalization analysis: MUC1, CD40, and PRKAB1. Validation results showed that 88.79% of DEGs were replicated in the FAH-SYS cohort. Associations between pairs of MUC1-Bacillus aciditolerans and PRKAB1-Escherichia coli in the FAH-SYS cohort were consistent with eQTL-mbQTL colocalization. CONCLUSIONS: This multi-omics integration study highlighted that OS genes causal to CD are regulated by DNA methylation and host-microbiota interactions. This provides evidence for future targeted functional research aimed at developing suitable therapeutic interventions and disease prevention.

LED lighting area recognition for visible light positioning based on convolutional neural network in the industrial internet of things.

In the industrial environment, the positioning of mobile terminals plays an important role in production scheduling. Visible light positioning (VLP) based on a CMOS image sensor has been widely considered as a promising indoor positioning technology. However, the existing VLP technology still faces many challenges, such as modulation and decoding schemes, and strict synchronization requirements. In this paper, a visible light area recognition framework based on convolutional neural network (CNN) is proposed, where the training data is the LED images acquired by the image sensor. The mobile terminal positioning can be realized from the perspective of recognition without modulating LED. The experimental results show that the mean accuracy of the optimal CNN model is as high as 100% for the two-class and the four-class area recognitions, and is more than 95% for the eight-class area recognition. These results are obviously superior to other traditional recognition algorithms. More importantly, the model has high robustness and universality, which can be applied to various types of LED lights.

Visible light sensing based on shadow features using multi-scale region convolutional neural network.

There are various production items in the industrial internet of things (IIoT) environment, such as pedestrians, robots, automated automated guided vehicles, etc. The practice industrial environment requires simultaneous communication and sensing of production items to achieve intelligent production and control. Thus, sensing methods not only require the integration of communication but also achieve sensing tasks such as recognition and positioning. Compared with traditional sensing media, visible light sensing has the advantages of high-speed communication, high sensing accuracy, and security, low energy consumption, and has become a potential sensing technology. Based on the strong directivity of visible light spatial radiation and the consistency of light intensity and position, this paper proposes a multi-scale visible light sensing-region convolutional neural network (VLS-RCNN) framework based on shadow features for multiple target sensing. The framework enables the recognition and positioning to use shared visible light shadow features to assist each other, and the multi-scale compensation strategy of the shadow region makes the framework more robust. The simulation results show that positioning results in the sensing area improve the recognition accuracy. The recognition results also reduce the positioning error without additional overhead. Therefore, this paper provides a new perspective for the sensing technology in the future IIoT, which should be considered to sense objects of interest by utilizing the inherent characteristics of visible light.

RIS-aided dynamically adaptive wide field-of-view receiver for visible light communication in industrial internet of things.

In the current visible light communication (VLC) system, a condenser lens is generally used in the front of receiver to achieve a higher data rate, making an extremely narrow field-of-view for the receiver. With the spread of Industrial Internet of Things (IIoT), the communication between mobile terminals is urgently required. A wide-range detecting method for VLC system in IIoT scenario is asked. In this paper, a novel self-adaptive wide-FoV receiver involving reconfigurable intelligent surfaces (RIS) is proposed. The effective detecting range of the receiver can be expanded by dynamically adjusting the incident light directions with the assistance of RIS. Based on the maximum arrived flux criterion, the mathematical model is established and the optimized RIS parameter tuning algorithm is presented. The feasibility and validity of the method are verified by simulation. The results show that the tolerable transceiver offset can be increased to 2∼4 times as the conventional receiver.

Mobile recognition and positioning for multiple visible light communication cells using a convolutional neural network.

The industrial Internet of Things (IIoT) environment involves multiple production items, such as robots and automated guided vehicles (AGVs), among others. The practical industrial scenario requires communication of production items while also considering mobile recognition and positioning. Hence the perception approach requires not only combining communications but also realizing the recognition and positioning of multiple communication cells. This Letter proposes a multi-optical cell recognition and positioning framework based on LED image features. The LED images are obtained by a CMOS image sensor. This framework utilizes convolutional neural networks (CNN) to train LED images for recognition between multiple optical cells and locates precise positions through region recognition within the optical cells. The experimental results show that the mean accuracy of the CNN model for two LED cells is above 99%, and the mean accuracy of region recognition within the optical cell is as high as 100%, which is significantly better than other traditional recognition algorithms. Therefore, the proposed framework can provide location-aware services for visible light communication and has a wide application prospect in IIoT.

Predictive Model for PICC Occlusion Risk for Patients in Intensive Care Units: A Retrospective Clinical Study.

Context: Peripherally inserted central catheters (PICCs) have a high incidence of catheter occlusion, but research exploring the risk factors for such an occlusion for patients in intensive care units (ICUs) is lacking. Objective: The study intended to examine the impact of multiple risk factors on the occurrence of PICC catheter occlusion to find evidence that can help clinical medical staff identify patients at an early stage who are at high risk of a catheter occlusion. Design: The research team performed a retrospective, observational clinical study. Setting: The study took place at a tertiary general hospital, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University in Wenzhou, China. Participants: Participants were 300 patients with a PICC who received treatment in the hospital's adult ICU between January 2019 and April 2022. Groups: According to the time of catheterization, the research team numbered the 1~300 participants and then selected one starting number to divided them into two groups according to the random number table. These two groups were: (1) a training group with 225 participants and (2) validation group with 75 participants. Outcome Measures: The main outcome measure was the evaluation of the factors impacting patients who had had a PICC occlusion during catheter retention, including complete and incomplete occlusions, to build a risk prediction model of PICC occlusion. A secondary outcome measure was the occurrence of extubation of the PICC discharge of the ICU patient. The research team performed a univariate analysis of the training group's data and a multivariate logistic regression analysis of the risk factors. The team: (1) built a risk prediction model of PICC occlusion using the independent risk factors for catheter occlusion for PICC patients in an ICU and (2) used the Hosmer-Lemeshow goodness-of-fit test to test the prediction model. A two tailed using p>0.05 indicated that the model had a good fit. Then, the team applied the model to the validation group and evaluated the model's predictive ability using a receiver operating characteristic (ROC) curve. The team considered an area under the curve (AUC) >0.5 to have predictive value. The larger the area was, the better the predicted value was. The incidence of PICC occlusion in the training group was 18.22%, including 10 participants with complete occlusion and 31 with partial occlusion. The team used the SPSS 22.0 and R software for statistical analysis. Results: The univariate analysis showed that 13 factors were associated with PICC occlusion, including: (1) an age ≥65 years (P < .001), a BMI of ≥24 kg/m2 (P < .001), (2) a BMI of ≥24kg/m2 (P = .002), (3) diabetes (P < .001), (4) stroke (P < .001), (5) hypertension (P < .001), (6) malignant tumors (P < .001), (7) a history of deep vein thrombosis (P < .001), (8) limb activity (P < .001), (10) flushing and sealing pipe frequency of Q8h (P = .035), (11) retention time (P < .001), (12) an increased platelet count (P = .036), (13) blood transfusions (P < .001), and (14) intravenous nutrition (P < .001). The independent risk factors for PICC occlusion included: (1) age ≥65 years-OR=1.224, P = .028; (2) BMI ≥24 kg/m2-OR=1.679, P = .004; (3) diabetes-OR=1.343, P = .017; (4) malignant tumors-OR=2.736, P < .001; (5) blood transfusions-OR=1.947, P < .001), and (6) intravenous nutrition-OR=2.021, P < .001. The frequency of flushing and sealing the pipe (Q8h)-OR=-2.145, P = .002-was a protective factor. In the training group, the area under the curve (AUC) for predicting a PICC occlusion was 0.917. The Hosmer-Lemeshow test of the prediction model showed that no significant differences existed in the test results within the model (χ2 = 5.830, P = .666), indicating that the model passed the internal validation. The ideal and calibration curves of the prediction model were highly coincident, and the model was well calibrated. The Hosmer-Lemeshow test of the validation group showed that no significant differences existed in the test results outside the model, suggesting that the model had high consistency. Conclusions: Age ≥65 years, BMI ≥24 kg/m2, diabetes, malignant tumors, blood transfusions, and intravenous nutrition were independent risk factors for PICC occlusion, while the frequency of flushing and sealing pipe (Q8h) was a protective factor. This prediction model had an outstanding ability to discriminate in identifying patients with a high-risk of PICC occlusion in the ICU.

Adaptive blind receiving for deep VLC coverage in non-uniform LED systems.

Recently, visible light communication (VLC) has become a promising technology for reliable communication in the industrial Internet of Things. In VLC, light sources with non-uniform parameters make uncontrollable signal blind zones and aliases. The unknown parameters also lead to the problem of channel estimation. Hence, deep coverage of VLC is challenging. In this paper, we design a dual-sensor elongated receiver. The receiver expands the signal receiving area by increasing the distance between photon detectors and realizes aliased signal recovery without channel estimation. Cooperatively, an adaptive blind receiving scheme is proposed. The scheme separates aliased signals by parameter estimation of a Gaussian mixture model without knowing transmitter parameters. It unifies the aliased and blind receiving cases by an adaptive signal combining method. Finally, the simulation results demonstrate that our scheme achieves better reliability and deeper communication coverage in the given system.

IRF1 as a potential biomarker in Mycobacterium tuberculosis infection.

Pulmonary tuberculosis (PTB) is a major global public health problem. The purpose of this study was to find biomarkers that can be used to diagnose tuberculosis. We used four NCBI GEO data sets to conduct analysis. Among the four data sets, GSE139825 is lung tissue microarray, and GSE83456, GSE19491 and GSE50834 are blood microarray. The differential genes of GSE139825 and GSE83456 were 68 and 226, and intersection genes were 11. Gene ontology (GO) analyses of 11 intersection genes revealed that the changes were mostly enriched in regulation of leucocyte cell-cell adhesion and regulation of T-cell activation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs revealed that the host response in TB strongly involves cytokine-cytokine receptor interactions and folate biosynthesis. In order to further narrow the range of biomarkers, we used protein-protein interaction to establish a hub gene network of two data sets and a network of 11 candidate genes. Eventually, IRF1 was selected as a biomarker. As validation, IRF1 levels were shown to be up-regulated in patients with TB relative to healthy controls in data sets GSE19491 and GSE50834. Additionally, IRF1 levels were measured in the new patient samples using ELISA. IRF1 was seen to be significantly up-regulated in patients with TB compared with healthy controls with an AUC of 0.801. These results collectively indicate that IRF1 could serve as a new biomarker for the diagnosis of pulmonary tuberculosis.

Cholesterol-Modified Oligonucleotides as Internal Reaction Controls during DNA-Encoded Chemical Library Synthesis.

We report two cholesterol-modified oligonucleotides for use as internal controls for on-DNA reactions during the pooled stages of a DNA-encoded chemical library (DECL) synthesis. As these cholesterol-tagged oligonucleotides are chromatographically separable from normal DECL intermediates, they can be directly monitored by mass spectrometry to track reaction progression within a complex pool of DNA. We observed similar product conversions for reactions on substrates linked to a standard DECL DNA headpiece, to the cholesterol-modified oligonucleotides, and to the cholesterol-modified oligonucleotides while in the presence of pooled DECL synthetic intermediates-validating their use as a representative control. We also highlight an example from a DECL production in which the use of the cholesterol-modified oligonucleotides provided quality control information that guided synthetic decisions. We conclude that the use of cholesterol-modified oligonucleotides as a regular control will significantly improve the quality of DECL productions.

Highly Robust Non-Noble Alkaline Hydrogen-Evolving Electrocatalyst from Se-Doped Molybdenum Disulfide Particles on Interwoven CoSe2 Nanowire Arrays.

Developing efficient non-noble and earth-abundant hydrogen-evolving electrocatalysts is highly desirable for improving the energy efficiency of water splitting in base. Molybdenum disulfide (MoS2 ) is a promising candidate, but its catalytic activity is kinetically retarded in alkaline media due to the unfavorable water adsorption and dissociation feature. A heterogeneous electrocatalyst is reported that is constructed by selenium-doped MoS2 (Se-MoS2 ) particles on 3D interwoven cobalt diselenide (CoSe2 ) nanowire arrays that drives the hydrogen evolution reaction (HER) with fast reaction kinetics in base. The resultant Se-MoS2 /CoSe2 hybrid exhibits an outstanding catalytic HER performance with extremely low overpotentials of 30 and 93 mV at 10 and 100 mA cm-2 in base, respectively, which outperforms most of the inexpensive alkaline HER catalysts, and is among the best alkaline catalytic activity reported so far. Moreover, this hybrid catalyst shows exceptional catalytic performance with very low overpotentials of 84 and 95 mV at 10 mA cm-2 in acidic and neutral electrolytes, respectively, implying robust pH universality of this hybrid catalyst. This work may provide new inspirations for the development of high-performance MoS2 -based HER electrocatalysts in unfavorable basic media for promising catalytic applications.

Tri-layer gradient and polarization-selective vertical couplers for interlayer transition.

We demonstrate and optimize a tri-layer vertical coupler for a silicon nitride (Si3N4) multilayer platform operating at a 2 µm band. The large spacing between the topmost and bottommost layers of a gradient structure enables ultra-low crossing loss and interlayer crosstalk without affecting the efficiency interlayer transition. We achieve a 0.31 dB transition loss, ultra-low multi-layer crosstalk of -59.3 dB at a crossing angle of 90° with an interlayer gap of 2300 nm at 1950nm. With width optimization of this structure, the fabrication tolerances toward lateral misalignment of two stages in this coupler have increased 61% and 56%, respectively. We also propose a vertical coupler, based on this design, with mode selectivity and achieve an extinction ratio of < 15 dB for wavelengths in the 1910-1990 range. Meanwhile, a multi-layer interlaced AWGs centered at 1950nm and based on vertical coupler has been demonstrated. The proposed vertical couplers exhibit potential for application in large-scale photonic-integrated circuits and broadly in photonic devices.

Ultrasound measurement of laryngeal structures in the parasagittal plane for the prediction of difficult laryngoscopies in Chinese adults.

BACKGROUND: Abnormal laryngeal structures are likely to be associated with a difficult laryngoscopy procedure. Currently, laryngeal structures can be measured by ultrasonography, however, little research has been performed on the potential role of ultrasound on the evaluation of a difficult laryngoscopy. The present study investigated the value of laryngeal structure measurements for predicting a difficult laryngoscopy. OBJECTIVE: The main objective of this study was to explore the value of laryngeal structure measurements for predicting a difficult laryngoscopy. METHODS: Two hundred and eleven adult patients (over 18 years old) were recruited to undergo elective surgery under general anesthesia via endotracheal intubation. Ultrasound was utilized to measure the distance between the skin and thyroid cartilage (DST), the distance between the thyroid cartilage and epiglottis (DTE), and the distance between the skin and epiglottis (DSE) in the parasagittal plane. These metrics were then investigated as predictors for classifying a laryngoscopy as difficult vs easy, as defined by the Cormack and Lehane grading scale. RESULTS: Multivariate logistic regression showed that the DSE, but not DST or DTE, was significantly related to difficult laryngoscopies. Specifically, a DSE ≥ 2.36 cm predicted difficult laryngoscopies with a sensitivity and specificity of 0.818 (95% CI: 0.766-0.870) and 0.856 (95% CI: 0.809-0.904). Furthermore, when combining the best model constructed of other indicators (i.e. sex, body mass index, modified Mallampati test) to predict the difficult laryngoscopy, the AUC reached 93.28%. CONCLUSION: DSE is an independent predictor of a difficult laryngoscopy; a DSE cutoff value of 2.36 cm is a better predictor of a difficult laryngoscope than other ultrasound or physiological measurements for predicting a difficult laryngoscope. Nevertheless, it's more valuable to apply the best model of this study, composed of various physiological measurements, for this prediction purpose.

Adaptive spatial-layout selection for massive multi-color visible light communications.

Massive multi-color visible light communications (mMC-VLC) sufficiently utilizing space and color domain resources is proposed to satisfy high-spectral-efficiency, high-speed, and high-density-coverage requirements of next-generation indoor data connections. However, the gap between the number of LEDs and photodiodes (PDs) and the high correlation among different channels limit the multiplexing of mMC-VLC. Also, the mobility of the receiver is the bottleneck of mMC-VLC. So, adaptive spatial-layout selection (ASLS) is proposed to settle the above problems, which selects $ N $N sets $ n $n-color LEDs from the transmitter to form an approximate optimal closed-circle layout adapting to the receiver position. First, the optimal parameter problems to minimize the ill condition of the activated system under layout constraints of the closed circle and linear types are formulated for a fixed receiver position. Second, to achieve adaptivity, the fitting curves of the optimal layout parameter and $ {D_v} $Dv under both constraints are researched; $ {D_v} $Dv is the vertical distance between the transmitter and receiver planes. Finally, the closest layout-selected principle (CLSP) is proposed to solve the problem of the LEDs perhaps not perfectly forming the optimal parameter layout for mMC-VLC. The bit-error ratio (BER) performances and application scopes of ASLS under both layout constraints are compared to determine that the constraint layout is a closed circle; meanwhile the available maximal $ N $N corresponding to the receiver position is obtained. The optimal parameter of ASLS is linearly related to the receiver position and not related to $ N $N. The ASLS always achieves better BER performance than optical multi-stream spatial modulation.

Fast-adaptive color-collaborative constellation designs for multicolor multiple-input multiple-output visible light communications systems.

A fast-adaptive color-collaborative signal constellation (CCSC) is proposed for chromaticity-changeable multicolor multiple-input multiple-output (MIMO) visible light communications (VLC) systems. The design of CCSC aims at developing the constellation to minimize the average optical power with shaping-based lattice code. For multicolor VLC signals, we arrive at the fact that the optimum shape of the constellation bounding region is an inequilateral N-D simplex. With the densest lattice-packing structure, detailed design steps of CCSC are fully elaborated by lattice construction, effective point selection, and chromaticity adjustment. As CCSC has a low complexity, its constellation structure can adapt quickly to changeable chromaticity demands. Simulation results show that the performance of CCSC is better than enhanced standard color shift keying (CSK) and is close to iterative CSK modulation based on numerical search.

One symbol training receiver for the SPAD-based UVLC system.

For the long distance single photon avalanche diode (SPAD)-based underwater visible light communication (UVLC) system, the multiple-symbol union detection scheme is presented. However, an error floor curve of the bit-to-error ratio (BER) occurs and cannot vanish even though the transmitted power approaches infinity. In this paper, to solve the problems of error floor and channel estimation in the long distance SPAD-based UVLC system, we propose the one training symbol maximum likelihood (ML) detection receiver. First, we add one training symbol in the head of each frame to eliminate the error floor and ensure the reliable blind estimation of the long distance UVLC channel. Meanwhile, the training ML detection (TMLD) receiver is developed. And then, to improve the system performance, a training modified (TM) quasi-ML receiver with Anscombe root transformation is proposed. Compared to the traditional mean detection scheme of the SPAD receiver, the simulation results show that the proposed TM quasi-ML and TMLD schemes significantly improve the error-rate performance.