A Mid-Infrared Perfect Metasurface Absorber with Tri-Band Broadband Scalability.

Metasurfaces have emerged as a unique group of two-dimensional ultra-compact subwavelength devices for perfect wave absorption due to their exceptional capabilities of light modulation. Nonetheless, achieving high absorption, particularly with multi-band broadband scalability for specialized scenarios, remains a challenge. As an example, the presence of atmospheric windows, as dictated by special gas molecules in different infrared regions, highly demands such scalable modulation abilities for multi-band absorption and filtration. Herein, by leveraging the hybrid effect of Fabry-Perot resonance, magnetic dipole resonance and electric dipole resonance, we achieved multi-broadband absorptivity in three prominent infrared atmospheric windows concurrently, with an average absorptivity of 87.6% in the short-wave infrared region (1.4-1.7 µm), 92.7% in the mid-wave infrared region (3.2-5 µm) and 92.4% in the long-wave infrared region (8-13 µm), respectively. The well-confirmed absorption spectra along with its adaptation to varied incident angles and polarization angles of radiations reveal great potential for fields like infrared imaging, photodetection and communication.

Nonlinear encoding in diffractive information processing using linear optical materials.

Nonlinear encoding of optical information can be achieved using various forms of data representation. Here, we analyze the performances of different nonlinear information encoding strategies that can be employed in diffractive optical processors based on linear materials and shed light on their utility and performance gaps compared to the state-of-the-art digital deep neural networks. For a comprehensive evaluation, we used different datasets to compare the statistical inference performance of simpler-to-implement nonlinear encoding strategies that involve, e.g., phase encoding, against data repetition-based nonlinear encoding strategies. We show that data repetition within a diffractive volume (e.g., through an optical cavity or cascaded introduction of the input data) causes the loss of the universal linear transformation capability of a diffractive optical processor. Therefore, data repetition-based diffractive blocks cannot provide optical analogs to fully connected or convolutional layers commonly employed in digital neural networks. However, they can still be effectively trained for specific inference tasks and achieve enhanced accuracy, benefiting from the nonlinear encoding of the input information. Our results also reveal that phase encoding of input information without data repetition provides a simpler nonlinear encoding strategy with comparable statistical inference accuracy to data repetition-based diffractive processors. Our analyses and conclusions would be of broad interest to explore the push-pull relationship between linear material-based diffractive optical systems and nonlinear encoding strategies in visual information processors.

Pyramid diffractive optical networks for unidirectional image magnification and demagnification.

Diffractive deep neural networks (D2NNs) are composed of successive transmissive layers optimized using supervised deep learning to all-optically implement various computational tasks between an input and output field-of-view. Here, we present a pyramid-structured diffractive optical network design (which we term P-D2NN), optimized specifically for unidirectional image magnification and demagnification. In this design, the diffractive layers are pyramidally scaled in alignment with the direction of the image magnification or demagnification. This P-D2NN design creates high-fidelity magnified or demagnified images in only one direction, while inhibiting the image formation in the opposite direction-achieving the desired unidirectional imaging operation using a much smaller number of diffractive degrees of freedom within the optical processor volume. Furthermore, the P-D2NN design maintains its unidirectional image magnification/demagnification functionality across a large band of illumination wavelengths despite being trained with a single wavelength. We also designed a wavelength-multiplexed P-D2NN, where a unidirectional magnifier and a unidirectional demagnifier operate simultaneously in opposite directions, at two distinct illumination wavelengths. Furthermore, we demonstrate that by cascading multiple unidirectional P-D2NN modules, we can achieve higher magnification factors. The efficacy of the P-D2NN architecture was also validated experimentally using terahertz illumination, successfully matching our numerical simulations. P-D2NN offers a physics-inspired strategy for designing task-specific visual processors.

All-optical phase conjugation using diffractive wavefront processing.

Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with applications ranging from imaging to beam focusing. Here, we present a diffractive wavefront processor to approximate all-optical phase conjugation. Leveraging deep learning, a set of diffractive layers was optimized to all-optically process an arbitrary phase-aberrated input field, producing an output field with a phase distribution that is the conjugate of the input wave. We experimentally validated this wavefront processor by 3D-fabricating diffractive layers and performing OPC on phase distortions never seen during training. Employing terahertz radiation, our diffractive processor successfully performed OPC through a shallow volume that axially spans tens of wavelengths. We also created a diffractive phase-conjugate mirror by combining deep learning-optimized diffractive layers with a standard mirror. Given its compact, passive and multi-wavelength nature, this diffractive wavefront processor can be used for various applications, e.g., turbidity suppression and aberration correction across different spectral bands.

All-optical complex field imaging using diffractive processors.

Complex field imaging, which captures both the amplitude and phase information of input optical fields or objects, can offer rich structural insights into samples, such as their absorption and refractive index distributions. However, conventional image sensors are intensity-based and inherently lack the capability to directly measure the phase distribution of a field. This limitation can be overcome using interferometric or holographic methods, often supplemented by iterative phase retrieval algorithms, leading to a considerable increase in hardware complexity and computational demand. Here, we present a complex field imager design that enables snapshot imaging of both the amplitude and quantitative phase information of input fields using an intensity-based sensor array without any digital processing. Our design utilizes successive deep learning-optimized diffractive surfaces that are structured to collectively modulate the input complex field, forming two independent imaging channels that perform amplitude-to-amplitude and phase-to-intensity transformations between the input and output planes within a compact optical design, axially spanning ~100 wavelengths. The intensity distributions of the output fields at these two channels on the sensor plane directly correspond to the amplitude and quantitative phase profiles of the input complex field, eliminating the need for any digital image reconstruction algorithms. We experimentally validated the efficacy of our complex field diffractive imager designs through 3D-printed prototypes operating at the terahertz spectrum, with the output amplitude and phase channel images closely aligning with our numerical simulations. We envision that this complex field imager will have various applications in security, biomedical imaging, sensing and material science, among others.

Occurrence of microplastics in the Haima cold seep area of the South China Sea.

The pollution of deep-sea microplastics has received increasing attention. As a special ecosystem in the deep sea, the cold seep area is of great significance for studying the distribution of microplastics in the deep sea. In this work, the distribution and characteristics of microplastics in seawater, sediments, and shellfish in the Haima cold seep area and the correlation between the characteristics of microplastics in different media and the type of media were studied. Microplastics were found in all three media. The abundance of microplastics in different samples from the Haima cold seep area ranged 1.8-3.8 items/L for the seawater, 11.47-96.8 items/kg (d.w.) for the surface sediments, and 0-5 items/individual (0-0.714 items/g) for the shellfish. The amount of microplastics ingested by shellfish varied among different species. The microplastics in these three media were mainly fibrous, dark-colored, small-sized rayon, polyethylene terephthalate (PET), and polyethylene (PE). In the correlation analysis of microplastic characteristics among the three media, it was found that the characteristics of microplastics in different media in the same area were closely related, and each pair of variables showed a significant positive correlation (P ≤ 0.05). The distinctive geographical conditions would accelerate the interchange of microplastics among various media. Principal component analysis showed that habitat contribute to microplastic feature differences in shellfish. Differences in correlation were observed between the characteristics of shellfish microplastics in different regions and the characteristics of microplastics in surrounding seawater and sediments.

4-Fluoro-2-(phenyl-amino)-benzoic acid.

The title compound, C13H10FNO2, was obtained by the reaction of 2-bromo-4-fluoro-benzoic acid with aniline. There are two independent mol-ecules, A and B, in the asymmetric unit, with slight conformational differences: the dihedral angles between the aromatic rings are 55.63 (5) and 52.65 (5)°. Both mol-ecules feature an intra-molecular N-H⋯O hydrogen bond. In the crystal, the mol-ecules are linked by pairwise O-H⋯O hydrogen bonds to form A-B acid-acid dimers and weak C-H⋯F inter-actions further connect the dimers.

Predicting Drug-Drug Interactions Involving Rifampicin Using a Semi-mechanistic Hepatic Compartmental Model.

AIMS: To develop a semi-mechanistic hepatic compartmental model to predict the effects of rifampicin, a known inducer of CYP3A4 enzyme, on the metabolism of five drugs, in the hope of informing dose adjustments to avoid potential drug-drug interactions. METHODS: A search was conducted for DDI studies on the interactions between rifampicin and CYP substrates that met specific criteria, including the availability of plasma concentration-time profiles, physical and absorption parameters, pharmacokinetic parameters, and the use of healthy subjects at therapeutic doses. The semi-mechanistic model utilized in this study was improved from its predecessors, incorporating additional parameters such as population data (specifically for Chinese and Caucasians), virtual individuals, gender distribution, age range, dosing time points, and coefficients of variation. RESULTS: Optimal parameters were identified for our semi-mechanistic model by validating it with clinical data, resulting in a maximum difference of approximately 2-fold between simulated and observed values. PK data of healthy subjects were used for most CYP3A4 substrates, except for gilteritinib, which showed no significant difference between patients and healthy subjects. Dose adjustment of gilteritinib co-administered with rifampicin required a 3-fold increase of the initial dose, while other substrates were further tuned to achieve the desired drug exposure. CONCLUSIONS: The pharmacokinetic parameters AUCR and CmaxR of drugs metabolized by CYP3A4, when influenced by Rifampicin, were predicted by the semi-mechanistic model to be approximately twice the empirically observed values, which suggests that the semi-mechanistic model was able to reasonably simulate the effect. The doses of four drugs adjusted via simulation to reduce rifampicin interaction.

Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling.

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model links the concentration-time profile of a drug with its therapeutic effects based on the underlying biological or physiological processes. Clinical endpoints play a pivotal role in drug development. Despite the substantial time and effort invested in screening drugs for favourable pharmacokinetic (PK) properties, they may not consistently yield optimal clinical outcomes. Furthermore, in the virtual compound screening phase, researchers cannot observe clinical outcomes in humans directly. These uncertainties prolong the process of drug development. As incorporation of Artificial Intelligence (AI) into the physiologically based pharmacokinetic/pharmacodynamic (PBPK) model can assist in forecasting pharmacodynamic (PD) effects within the human body, we introduce a methodology for utilizing the AI-PBPK platform to predict the PK and PD outcomes of target compounds in the early drug discovery stage. In this integrated platform, machine learning is used to predict the parameters for the model, and the mechanism-based PD model is used to predict the PD outcome through the PK results. This platform enables researchers to align the PK profile of a drug with desired PD effects at the early drug discovery stage. Case studies are presented to assess and compare five potassium-competitive acid blocker (P-CAB) compounds, after calibration and verification using vonoprazan and revaprazan.

Selective Extraction of Critical Metals from Spent Li-Ion Battery Cathode: Cation-Anion Coordination and Anti-Solvent Crystallization.

Owing to continuing global use of lithium-ion batteries (LIBs), in particular in electric vehicles (EVs), there is a need for sustainable recycling of spent LIBs. Deep eutectic solvents (DESs) are reported as "green solvents" for low-cost and sustainable recycling. However, the lack of understanding of the coordination mechanisms between DESs and transition metals (Ni, Mn and Co) and Li makes selective separation of transition metals with similar physicochemical properties practically difficult. Here, it is found that the transition metals and Li have a different stable coordination structure with the different anions in DES during leaching. Further, based on the different solubility of these coordination structures in anti-solvent (acetone), a leaching and separation process system is designed, which enables high selective recovery of transition metals and Li from spent cathode LiNi1/3Co1/3Mn1/3O2 (NCM111), with recovery of acetone. Recovery of spent LiCoO2 (LCO) cathode is also evidenced and a significant selective recovery for Co and Li is established, together with recovery and reuse of acetone and DES. It is concluded that the tuning of cation-anion coordination structure and anti-solvent crystallization are practical for selective recovery of critical metal resources in the spent LIBs recycling.

Virtual histological staining of unlabeled autopsy tissue.

Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.

All-optical image denoising using a diffractive visual processor.

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts from input images. In general, digital image denoising algorithms, executed on computers, present latency due to several iterations implemented in, e.g., graphics processing units (GPUs). While deep learning-enabled methods can operate non-iteratively, they also introduce latency and impose a significant computational burden, leading to increased power consumption. Here, we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images - implemented at the speed of light propagation within a thin diffractive visual processor that axially spans <250 × λ, where λ is the wavelength of light. This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features, causing them to miss the output image Field-of-View (FoV) while retaining the object features of interest. Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of ~30-40%. We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum. Owing to their speed, power-efficiency, and minimal computational overhead, all-optical diffractive denoisers can be transformative for various image display and projection systems, including, e.g., holographic displays.

Association between sugar-free beverage intake and childhood obesity among Chinese children and adolescents.

BACKGROUND: The relationship between sugar-free beverage (SFB) intake and childhood obesity among Chinese children is unknown. OBJECTIVES: To describe the status of SFB consumption among children and adolescents in China and assess the association between SFB intake and different types of obesity. METHODS: The study was based on the baseline data of an ongoing cohort project named Evaluation and Monitoring on School-based Nutrition and Growth in Shenzhen (EMSNGS). Food frequency questionnaires were used to collect information on SFB consumption in 3227 students aged 9-17. Physical and clinical examinations were conducted by trained investigators and clinicians. Multivariable binary logistic regression models were performed to assess the association between SFB intake and general obesity, overweight/obesity, abdominal obesity, metabolically unhealthy overweight (MUOW)/metabolically unhealthy obesity (MUO). RESULTS: The median age of the participants was 13.28 years. Among the participants, 55.2% were boys, and 66.1% were adolescents. The median SFB consumption was 16.67 mL/d. After adjusting for potential confounding factors, each 100 mL increase in daily SFB intake was associated with an increased risk of overweight/obesity (OR = 1.14; 95%CI: 1.06-1.23), abdominal obesity (OR = 1.12; 95%CI: 1.03-1.23), and MUOW/MUO (OR = 1.12; 95%CI: 1.02-1.21), respectively. Stratified analyses showed that family income may have an impact on the association between SFB intake and overweight/obesity (P for interaction = 0.021) and abdominal obesity (P for interaction = 0.031). CONCLUSION: SFB intake was positively associated with childhood obesity in Chinese children, particularly among individuals with high-income families.

Associations of Combined Lifestyle Factors with MAFLD and the Specific Subtypes in Middle-Aged and Elderly Adults: The Dongfeng-Tongji Cohort Study.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is the crucial pathogenesis for intra-hepatic and extra-hepatic diseases, especially in elderly adults. Lifestyle management may be a modifiable cost-effective measure for MAFLD prevention, but the evidence is limited. A total of 23,408 middle-aged and elderly individuals were included in a longitudinal study from 2008 to 2018. Combined lifestyle scores (range 0-6) were evaluated by BMI, smoking, drinking, diet, physical activity, and sleep. Logistic regression models were used to calculate ORs for the risks of MAFLD and specific subtypes. The mean age of participants was 61.7 years, and 44.5% were men. Compared with poor lifestyle (scores 0-2), ORs (95% CIs) of the ideal lifestyle (scores 5-6) were 0.62 (0.57-0.68) for MAFLD, 0.31 (0.28-0.34) for MAFLD with excess weight and obesity, 0.97 (0.75-1.26) for MAFLD with diabetes, and 0.56 (0.51-0.62) for MAFLD with metabolic dysregulation. Additionally, lifestyle improvement was associated with lower risks of MAFLD (OR, 0.76; 95% CI, 0.68-0.86), MAFLD with excess weight and obesity (OR, 0.72; 95% CI, 0.63-0.81), MAFLD with diabetes (OR, 0.74; 95% CI, 0.54-1.02) and MAFLD with metabolic dysregulation (OR, 0.49; 95% CI, 0.43-0.55), respectively. Our findings suggest that adherence to a combined healthy lifestyle was associated with lower risks of MAFLD, particularly in excess weight/obese individuals or those with metabolic dysregulation.

Rapid sensing of hidden objects and defects using a single-pixel diffractive terahertz sensor.

Terahertz waves offer advantages for nondestructive detection of hidden objects/defects in materials, as they can penetrate most optically-opaque materials. However, existing terahertz inspection systems face throughput and accuracy restrictions due to their limited imaging speed and resolution. Furthermore, machine-vision-based systems using large-pixel-count imaging encounter bottlenecks due to their data storage, transmission and processing requirements. Here, we report a diffractive sensor that rapidly detects hidden defects/objects within a 3D sample using a single-pixel terahertz detector, eliminating sample scanning or image formation/processing. Leveraging deep-learning-optimized diffractive layers, this diffractive sensor can all-optically probe the 3D structural information of samples by outputting a spectrum, directly indicating the presence/absence of hidden structures or defects. We experimentally validated this framework using a single-pixel terahertz time-domain spectroscopy set-up and 3D-printed diffractive layers, successfully detecting unknown hidden defects inside silicon samples. This technique is valuable for applications including security screening, biomedical sensing and industrial quality control.

Abdominal obesity mediates the causal relationship between depression and the risk of gallstone disease: retrospective cohort study and Mendelian randomization analyses.

OBJECTIVE: Our study aimed to explore the causal effect of depression on the risk of gallstone disease, and the mediation effects of metabolic traits. METHODS: A retrospective cohort study on Chinese elderly from the Dongfeng-Tongji cohort (including 18,141 individuals) was conducted to estimate the adverse effect of probable depression on the risk of gallstone disease. Two-sample Mendelian randomization was performed in European and East-Asian ancestries, to verify the causal relationship between major depression and gallstone disease. We further applied two-step Mendelian randomization to explore the mediation effects of metabolic traits. RESULTS: In the cohort study, probable depression was associated with an increased risk of gallstone disease within 5 years, with RR (95% CI) of 1.33 (1.12, 1.58) in multivariable regression, and 1.34 (1.11, 1.61) following propensity score weighting. Bidirectional Mendelian randomization in European ancestry revealed a positive causal effect (OR: 1.21; 95% CI: 1.07 to 1.37) of genetically predicted major depression liability on gallstone disease, based on the inverse variance weighted method. Little evidence was presented from other complementary approaches, and the analysis in East-Asian ancestry (IVW estimated OR: 1.03; 95% CI: 0.92 to 1.15). The indirect effect via waist circumference and HDL-C were 1.06 (95% CI: 1.02 to 1.10) and 1.01 (95% CI: 1.00 to 1.01) respectively, which mediated 25.8% and 3.78% of the causal relationship. CONCLUSIONS: Our study suggested a higher risk of gallstone disease in the population with probable depression, while the two-sample Mendelian randomization provided weak evidence for the causal relationship, which was moderately mediated by abdominal obesity.

Quorum sensing bacteria in microplastics epiphytic biofilms and their biological characteristics which potentially impact marine ecosystem.

Microplastics (MPs) have been shown to be a new type of pollutant in the oceans, with complex biofilms attached to their surfaces. Bacteria with quorum sensing (QS) systems are important participants in biofilms. Such bacteria can secrete and detect signal molecules. When a signal molecule reaches its threshold level, bacteria with QS systems can perform several biological functions, such as biofilm formation and antibiotic metabolite production. However, the ecological effects of QS bacteria in biofilm as MPs distribute globally with ocean currents are not to be elucidate yet. In this study, polypropylene and polyvinyl chloride were selected for on-site enrichment to acquire microplastics with biofilms. Eight culturable QS bacteria in the resulting biofilm were isolated by using biosensor assays, and their biodiversity was analyzed. The profiles of the N-acyl-homoserine lactones (AHLs) produced by these bacteria were analyzed by using thin-layer chromatography (TLC)-bioautography and gas chromatography and mass spectrometry (GC-MS). Biofilm-forming properties and several biological characteristics, such as bacteriostasis, algal inhibition, and dimethylsulfoniopropionate (DMSP) degradation, were explored along with QS quenching. Results showed that QS bacteria were mainly affiliated with class Alphaproteobacteria, particularly Rhodobacteraceae, followed by class Gammaproteobacteria. TLC-bioautography and GC-MS analyses revealed that seven AHLs, namely, C6-HSL, C8-HSL, 3-oxo-C6-HSL, 3-oxo-C8-HSL, 3-oxo-C10-HSL, and two unidentified AHLs were produced. The QS system equipped bacteria with strong biofilm-forming capacity and may contribute to the keystone roles of Rhodobacteraceae. In addition, QS bacteria may exacerbate the adverse environmental effects of MPs, such as inducing the misfeeding of planktons on MPs. This study elucidated the diversity of QS bacteria in MP-associated biofilms and provided a new perspective of the effect of key membrane-forming bacteria on the marine ecological environment.

High-throughput terahertz imaging: progress and challenges.

Many exciting terahertz imaging applications, such as non-destructive evaluation, biomedical diagnosis, and security screening, have been historically limited in practical usage due to the raster-scanning requirement of imaging systems, which impose very low imaging speeds. However, recent advancements in terahertz imaging systems have greatly increased the imaging throughput and brought the promising potential of terahertz radiation from research laboratories closer to real-world applications. Here, we review the development of terahertz imaging technologies from both hardware and computational imaging perspectives. We introduce and compare different types of hardware enabling frequency-domain and time-domain imaging using various thermal, photon, and field image sensor arrays. We discuss how different imaging hardware and computational imaging algorithms provide opportunities for capturing time-of-flight, spectroscopic, phase, and intensity image data at high throughputs. Furthermore, the new prospects and challenges for the development of future high-throughput terahertz imaging systems are briefly introduced.

Universal Polarization Transformations: Spatial Programming of Polarization Scattering Matrices Using a Deep Learning-Designed Diffractive Polarization Transformer.

Controlled synthesis of optical fields having nonuniform polarization distributions presents a challenging task. Here, a universal polarization transformer is demonstrated that can synthesize a large set of arbitrarily-selected, complex-valued polarization scattering matrices between the polarization states at different positions within its input and output field-of-views (FOVs). This framework comprises 2D arrays of linear polarizers positioned between isotropic diffractive layers, each containing tens of thousands of diffractive features with optimizable transmission coefficients. After its deep learning-based training, this diffractive polarization transformer can successfully implement Ni No = 10 000 different spatially-encoded polarization scattering matrices with negligible error, where Ni and No represent the number of pixels in the input and output FOVs, respectively. This universal polarization transformation framework is experimentally validated in the terahertz spectrum by fabricating wire-grid polarizers and integrating them with 3D-printed diffractive layers to form a physical polarization transformer. Through this set-up, an all-optical polarization permutation operation of spatially-varying polarization fields is demonstrated, and distinct spatially-encoded polarization scattering matrices are simultaneously implemented between the input and output FOVs of a compact diffractive processor. This framework opens up new avenues for developing novel devices for universal polarization control and may find applications in, e.g., remote sensing, medical imaging, security, material inspection, and machine vision.

Universal linear intensity transformations using spatially incoherent diffractive processors.

Under spatially coherent light, a diffractive optical network composed of structured surfaces can be designed to perform any arbitrary complex-valued linear transformation between its input and output fields-of-view (FOVs) if the total number (N) of optimizable phase-only diffractive features is ≥~2NiNo, where Ni and No refer to the number of useful pixels at the input and the output FOVs, respectively. Here we report the design of a spatially incoherent diffractive optical processor that can approximate any arbitrary linear transformation in time-averaged intensity between its input and output FOVs. Under spatially incoherent monochromatic light, the spatially varying intensity point spread function (H) of a diffractive network, corresponding to a given, arbitrarily-selected linear intensity transformation, can be written as H(m, n; m', n') = |h(m, n; m', n')|2, where h is the spatially coherent point spread function of the same diffractive network, and (m, n) and (m', n') define the coordinates of the output and input FOVs, respectively. Using numerical simulations and deep learning, supervised through examples of input-output profiles, we demonstrate that a spatially incoherent diffractive network can be trained to all-optically perform any arbitrary linear intensity transformation between its input and output if N ≥ ~2NiNo. We also report the design of spatially incoherent diffractive networks for linear processing of intensity information at multiple illumination wavelengths, operating simultaneously. Finally, we numerically demonstrate a diffractive network design that performs all-optical classification of handwritten digits under spatially incoherent illumination, achieving a test accuracy of >95%. Spatially incoherent diffractive networks will be broadly useful for designing all-optical visual processors that can work under natural light.