Analyzing Data Modalities for Cattle Weight Estimation Using Deep Learning Models.

We investigate the impact of different data modalities for cattle weight estimation. For this purpose, we collect and present our own cattle dataset representing the data modalities: RGB, depth, combined RGB and depth, segmentation, and combined segmentation and depth information. We explore a recent vision-transformer-based zero-shot model proposed by Meta AI Research for producing the segmentation data modality and for extracting the cattle-only region from the images. For experimental analysis, we consider three baseline deep learning models. The objective is to assess how the integration of diverse data sources influences the accuracy and robustness of the deep learning models considering four different performance metrics: mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2). We explore the synergies and challenges associated with each modality and their combined use in enhancing the precision of cattle weight prediction. Through comprehensive experimentation and evaluation, we aim to provide insights into the effectiveness of different data modalities in improving the performance of established deep learning models, facilitating informed decision-making for precision livestock management systems.

Efficacy of Endopyelotomy for Secondary Pelvi-Ureteric Junction (PUJ) Obstruction in Terms of Improvement in Renal Function.

Objective The objective of this study is to measure renal function improvement after endopyelotomy for secondary pelvi-ureteric junction (PUJ) obstruction using technetium-99m diethylene-triamine-pentaacetate (DTPA) renal scintigraphy. Material and methods This descriptive study was carried out at the Department of Urology, Institute of Kidney Diseases, Peshawar, Pakistan from June 1, 2021, to May 31, 2023. The study included 118 secondary PUJ blockage patients who underwent endopyelotomy. Patient demographics, clinical history, and preoperative imaging findings were obtained. DTPA renal scintigraphy assessed renal function improvement postoperatively at intervals to determine the efficacy of endopyelotomy. Results The majority of the patients included in the study were male (n=65, 55.1%). The average age of the patients was 45.2 years, with the majority falling within the age range of 46-60 years (n=42, 35.6%). All patients had ultrasonography and computed tomography imaging done, and preoperative renal functions were obtained. Comorbidities included hypertension in 32 (27.12%) and diabetes in 18 (15.25%). DTPA renal scintigraphy showed improved renal function in 81.35% of patients at three months, 88.13% at six months, and 83.05% at 12 months; 15.3% of patients needed further treatments, and 5.1% had PUJ blockage recurrence. Conclusion This study offers significant insights into the results and complexities of endopyelotomy in patients suffering from PUJ blockage. The findings indicate that the technique efficiently enhances kidney function and alleviates symptoms in most patients. However, the study also emphasizes the need to monitor patients undergoing this procedure.

Rhodamine B for Probing the Effects of Modifications in Cetrimonium Bromide Counter-anions by Transition Metals on CTAB/Butanol/n-hexane/water Microemulsion.

Dye solubilization in microemulsion based on Cetyltrimethylammonium bromide (CTAB) and its modified forms (counter-anions based upon Zn2+, Cu2+ and Fe3+) is comparatively innovative and not explored in existing literature. Here, surfactant with modified counterions (SMCs) were used to study the effects of metal chlorides (ZnCl2, CuCl2 and FeCl3) modifications on the comparative solubilization of Rhodamine-B (RB) by Cetyltrimethylammonium bromide (CTAB) and its modified forms. The solubility of RB in different microemulsions were studied using UV-Visible spectroscopy and phase diagrams of CTAB with modified counter ions CTA+[ZnCl2.Br]- named as CZN-1, CTA+[CuCl2.Br]- named as CCU-1 and CTA+[FeCl3.Br]- named as CFE-1 based upon surfactant with modified counter ions (SMCs). Four different points in microemulsion region of phase diagram were selected with different percentage composition of Smix (surfactant and co-surfactant), oil and RB (taken as water component). The interaction of RB, CCU-1, CFE-1 and CZN-1 within microemulsion environment were studied using Fluorescence spectroscopy. Emission spectra of RB in CCU-1 and CFE-1 based microemulsion confirmed that RB formed complexes with Cu and Fe ions. It was also found that RB was less soluble in CTAB based microemulsion as compared to microemulsions based on SMCs. This novel research study will expose new path for future research work related to microemulsion.

Isolation and Screening of Zn (Zn) Solubilizing Rhizosphere Bacteria from Different Vegetations for Their Ability to Improve Growth, Zn Uptake, and Expression of Zn Transporter Genes in Tomato.

Zinc-solubilizing bacteria (ZSB) can convert insoluble zinc to an accessible form and increase Zn bioavailability in soil, which helps mitigate Zn deficiency in crops. In this study, different bacterial strains were screened for different Zn solubilization and plant growth promotion traits. Two bacterial strains, Acinetobacter pittii DJ55 and Stenotrophomonas maltophilia DJ24, were tested for their Zn-solubilizing potential on plate media, and both showed variable levels of Zn solubilization. The results showed that the bacterial strains applied to the plants in the pot experiment caused improvements in growth parameters compared to control conditions. DJ55, when applied with an insoluble source, enhanced plant height, leaf number, and leaf area compared to DJ24 and control conditions, while the maximum fruit weight was noticed in plants treated with ZnSO4. An increase in chlorophyll contents was noted in plants treated with ZnSO4, while maximum carotenoid contents were observed in plants treated with DJ55 + ZnO when compared with their controls. Plants supplemented with ZnO and DJ55 showed higher zinc content and iron content as compared to their respective controls. The expression patterns of the SLZIP5 and SLZIP4 genes were changed in the root and shoot. Application of ZnO stimulates both gene expression and protein synthesis in tomato roots and shoots. Inoculation of tomato plants with ZSB and insoluble ZnO reduced the expression of the SLZIP5 and SLZIP4 genes in the root and shoot. In conclusion, both strains can be considered as potential zinc-solubilizing bioinoculants to promote the growth and production yield of tomato.

Chemical insights into the synthetic chemistry of five-membered saturated heterocycles-a transition metal-catalyzed approach.

Drug design and delivery is primarily based on the hunt for new potent drug candidates and novel synthetic techniques. Recently, saturated heterocycles have gained enormous attention in medicinal chemistry as evidenced by the medicinal drugs listed in the FDA Orange Book. Therefore, the demand for novel saturated heterocyclic syntheses has increased tremendously. Transition metal (TM)-catalyzed reactions have remained the prime priority in heterocyclic syntheses for the last three decades. Nowadays, TM catalysis is well adorned by combining it with other techniques such as bio- and/or enzyme-catalyzed reactions, organocatalysis, or using two different metals in a single catalysis. This review highlights the recent developments of the transition metal-catalyzed synthesis of five-membered saturated heterocycles.

An insight into recent developments of copper, silver and gold carbon dots: cancer diagnostics and treatment.

Cancer is one of the most fatal diseases globally, however, advancement in the field of nanoscience specifically novel nanomaterials with nano-targeting of cancer cell lines has revolutionized cancer diagnosis and therapy and has thus attracted the attention of researchers of related fields. Carbon Dots (CDs)-C-based nanomaterials-have emerged as highly favorable candidates for simultaneous bioimaging and therapy during cancer nano-theranostics due to their exclusive innate FL and theranostic characteristics exhibited in different preclinical results. Recently, different transition metal-doped CDs have enhanced the effectiveness of CDs manifold in biomedical applications with minimum toxicity. The use of group-11 (Cu, Ag and Au) with CDs in this direction have recently gained the attention of researchers because of their encouraging results. This review summarizes the current developments of group-11 (Cu, Ag and Au) CDs for early diagnosis and therapy of cancer including their nanocomposites, nanohybrids and heterostructures etc. All The manuscript highlights imaging applications (FL, photoacoustic, MRI etc.) and therapeutic applications (phototherapy, photodynamic, multimodal etc.) of Cu-, Ag- and Au-doped CDs reported as nanotheranostic agents for cancer treatment. Sources of CDs and metals alogwith applications to give a comparative analysis have been given in the tabulated form at the end of manuscript. Further, future prospects and challenges have also been discussed.

An effective deep learning approach for the classification of Bacteriosis in peach leave.

Bacteriosis is one of the most prevalent and deadly infections that affect peach crops globally. Timely detection of Bacteriosis disease is essential for lowering pesticide use and preventing crop loss. It takes time and effort to distinguish and detect Bacteriosis or a short hole in a peach leaf. In this paper, we proposed a novel LightWeight (WLNet) Convolutional Neural Network (CNN) model based on Visual Geometry Group (VGG-19) for detecting and classifying images into Bacteriosis and healthy images. Profound knowledge of the proposed model is utilized to detect Bacteriosis in peach leaf images. First, a dataset is developed which consists of 10000 images: 4500 are Bacteriosis and 5500 are healthy images. Second, images are preprocessed using different steps to prepare them for the identification of Bacteriosis and healthy leaves. These preprocessing steps include image resizing, noise removal, image enhancement, background removal, and augmentation techniques, which enhance the performance of leaves classification and help to achieve a decent result. Finally, the proposed LWNet model is trained for leaf classification. The proposed model is compared with four different CNN models: LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The proposed model obtains an accuracy of 99%, which is higher than LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The achieved results indicate that the proposed model is more effective for the detection of Bacteriosis in peach leaf images, in comparison with the existing models.

Fetal Outcomes in Preterm Cesarean Sections.

Introduction Neonatal mortality is a major challenge in low-middle-income countries. The current study was conducted to assess the association between preterm cesarean delivery and fetal outcomes. Methods A prospective study was conducted at the Combined Military Hospital in Peshawar, Pakistan, from October 1, 2020, to March 31, 2021. All women reporting to the hospital with a cephalic presentation and singleton pregnancies between the 27th and 34th weeks of gestation were included in the study. Pregnancies with an abnormal presentation, those diagnosed with a congenital abnormality, and those with indications for growth restriction or preterm delivery were excluded from the study. We also excluded infants delivered via vacuum or forceps. The outcomes of interest in this study included neonatal death prior to discharge, neonatal respiratory distress, sepsis, intraventricular hemorrhage (IVH), seizure, subdural hemorrhage (SDH), or appearance, pulse, grimace, activity, and respiration (APGAR) test score of less than 7 at five minutes. Maternal features including diabetes, hypertension and gestational age of delivery, parity, previous cesarean sections (CS), and last pregnancy outcomes were documented in a predefined pro forma. Results Our sample size consisted of 288 women, who were classified into two groups. Group A comprised 144 women who gave birth vaginally and group B consisted of 144 women who underwent CS. It was observed that women who underwent cesareans had a higher likelihood of a history of hypertension and related pathologies. It was also observed that these women had a greater likelihood of being of higher age compared to women who underwent vaginal deliveries. Neonates of women who had CS were at a greater risk of presenting with respiratory distress than those who had spontaneous vaginal deliveries. Conclusion Based on our findings, respiratory distress was significantly more common in babies of women who delivered via CS. However, there was no difference in neonatal outcomes in terms of IVH, seizures, SDH, and APGAR score of <7.

HFM: A Hybrid Feature Model Based on Conditional Auto Encoders for Zero-Shot Learning.

Zero-Shot Learning (ZSL) is related to training machine learning models capable of classifying or predicting classes (labels) that are not involved in the training set (unseen classes). A well-known problem in Deep Learning (DL) is the requirement for large amount of training data. Zero-Shot learning is a straightforward approach that can be applied to overcome this problem. We propose a Hybrid Feature Model (HFM) based on conditional autoencoders for training a classical machine learning model on pseudo training data generated by two conditional autoencoders (given the semantic space as a condition): (a) the first autoencoder is trained with the visual space concatenated with the semantic space and (b) the second autoencoder is trained with the visual space as an input. Then, the decoders of both autoencoders are fed by the test data of the unseen classes to generate pseudo training data. To classify the unseen classes, the pseudo training data are combined to train a support vector machine. Tests on four different benchmark datasets show that the proposed method shows promising results compared to the current state-of-the-art when it comes to settings for both standard Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL).

Comparison Of Two Intra-Canal Medicaments On The Incidence Of Post-Operative Endodontic Pain.

BACKGROUND: Post-operative pain is the most annoying factor for the patients for which different intra-canal medicaments are used. The aim of this study was to compare calcium hydroxide mixed with dexamethasone and triple antibiotic paste as intra-canal medicaments on the incidence of post-operative endodontic pain. METHODS: This was a randomized controlled trail in which a total of 120 patients presenting to the department of Operative Dentistry AFID, Rawalpindi from 20-50 years of age from both gender presenting with acute apical periodontitis in their maxillary and mandibular central incisors were selected and randomly divided into three equal groups of 40 patients with the help of scientific random number table. Group A patients were given calcium hydroxide mixed with dexamethasone, group B patients were given triple antibiotic paste and in group C patients only cotton pellet was placed in the pulp chamber as a placebo. Endodontic therapy was initiated, following pulpectomy and disinfection, canals were prepared and intra-canal dressings were placed according to the allotted group and restored with cavit. Pain was recorded at 24, 48 and 72 hours using the visual analogue scale. Data was analysed using SPSS 21. RESULTS: After 72 hours, 55% of group A patients had no pain, 47.5% patients in group B while in group C 17.5% of the patients reported no pain. CONCLUSIONS: Both calcium hydroxide combined with dexamethasone and triple antibiotic paste are effective in reducing post-operative pain in teeth with symptomatic irreversible pulpitis while placebo showed little reduction in pain.

Edge-exposed MoS2 nanospheres assembled with SnS2 nanosheet to boost NO2 gas sensing at room temperature.

SnS2 nanosheets (NSs) have become an ideal candidate for high performance gas sensors due to their unique sensing properties. However, the restacking and aggregation in the process of sensor manufacturing have great influence on the gas sensing performance. In this study, we synthesized a novel heterojunction of the flower-like porous SnS2 NSs with edge exposed MoS2 nanospheres via a facile hydrothermal method and sensitive response has achieved at room temperature (27℃). After functionalization, the SMS-Ⅱ showed excellent response (Ra/Rg = 25.9-100 ppm NO2), which is 22.3 times higher than that of the pristine SnS2 NSs. The sensor also has the characteristics of short response time of 2 s, excellent base line recovery (28.2 s), long-term stability and reliability within 16 weeks, good selectivity and low detection concentration of only 50 ppb. The p-n heterojunction formed between the edge-exposed spherical MoS2 and the 3D flower-like SnS2 NSs has a synergistic effect, providing a highly active sites for the adsorption of NO2 gas, which greatly enhance the sensitivity of the sensor. Simple fabrication and excellent gas sensing performance of the SnS2/MoS2 heterostructure nanomaterials (NMs) will highly effective for commercial gas sensing application.

3D printing of silver-doped polycaprolactone-poly(propylene succinate) composite scaffolds for skin tissue engineering.

Scaffold-based tissue engineering approaches have been commonly used for skin regeneration or wound healings caused by diseases or trauma. For an ideal complete healing process, scaffold structures need to meet the criteria of biocompatibility, biodegradability, and antimicrobial properties, as well as to provide geometrical necessities for the regeneration of damaged tissue. In this study, design, synthesis and characterization of a three dimensional (3D) printable copolymer based on polycaprolactone-block-poly(1,3-propylene succinate) (PCL-PPSu) including anti-microbial silver particles is presented. 3D printing of PCL-PPSu copolymers provided a lower processing temperature compared to neat PCL, hence, inclusion of temperature-sensitive bioactive reagents into the developed copolymer could be realized. In addition, 3D printed block copolymer showed an enhanced hydrolytic and enzymatic degradation behavior. Cell viability and cytotoxicity of the developed copolymer were evaluated by using human dermal fibroblast (HDF) cells. The addition of silver nitrate within the polymer matrix resulted in a significant decrease in the adhesion of different types of microorganisms on the scaffold without inducing any cytotoxicity on HDF cells in vitro. The results suggested that 3D printed PCL-PPSu scaffolds containing anti-microbial silver particles could be considered as a promising biomaterial for emerging skin regenerative therapies, in the light of its adaptability to 3D printing technology, low-processing temperature, enhanced degradation behavior and antimicrobial properties.

Weakly-Supervised Network for Detection of COVID-19 in Chest CT Scans.

Deep Learning-based chest Computed Tomography (CT) analysis has been proven to be effective and efficient for COVID-19 diagnosis. Existing deep learning approaches heavily rely on large labeled data sets, which are difficult to acquire in this pandemic situation. Therefore, weakly-supervised approaches are in demand. In this paper, we propose an end-to-end weakly-supervised COVID-19 detection approach, ResNext+, that only requires volume level data labels and can provide slice level prediction. The proposed approach incorporates a lung segmentation mask as well as spatial and channel attention to extract spatial features. Besides, Long Short Term Memory (LSTM) is utilized to acquire the axial dependency of the slices. Moreover, a slice attention module is applied before the final fully connected layer to generate the slice level prediction without additional supervision. An ablation study is conducted to show the efficiency of the attention blocks and the segmentation mask block. Experimental results, obtained from publicly available datasets, show a precision of 81.9% and F1 score of 81.4%. The closest state-of-the-art gives 76.7% precision and 78.8% F1 score. The 5% improvement in precision and 3% in the F1 score demonstrate the effectiveness of the proposed method. It is worth noticing that, applying image enhancement approaches do not improve the performance of the proposed method, sometimes even harm the scores, although the enhanced images have better perceptual quality.

Phytochemical Screening, Antibacterial Activity and Heavy Metal Analysis of Ethnomedicinal Recipes and Their Sources Used Against Infectious Diseases.

Plants are a rich source of secondary metabolites that have been found to have medicinal properties. The present study was conducted to evaluate the phytochemical screening, antibacterial activities and heavy metal analysis of seven medicinal plants i.e., Nigella sativa (seeds), Trigonella foenum-graecum (seeds), Brassica campestris (seeds), Pistacia integerrima (galls), Linum usitatissimum (seeds), Hyssopus officinalis (flowers), Ephedra vulgaris (dry branches) and its two recipes which are used by hakims (Practitioners of local herbal medicines), against different diseases particularly respiratory tract infections. The obtained results revealed that alkaloids (30%) and flavonoids (41%) were in maximum quantity in P. integerrima (galls) while saponins (10.9%) were in maximum quantity in Recipe 1. The antibacterial activity was determined by the agar well disc diffusion method using methanol, ethanol, chloroform and deionized water extracts. Each plant extract was tested against one Gram-positive (Streptococcus pneumonia) and two Gram-negative (Pseudomonas aeruginosa and Klebsiella pneumonia) bacteria. Maximum zones of inhibition in methanol, ethanol, chloroform and aqueous extract were seen in T. foenum-graecum against S. pneumonia (20.06 ± 0.16 mm), B. campestris against S. pneumonia (22.40 ± 0.24 mm), Recipe 2 against K. pneumonia (20.06 ± 0.16 mm) and N. sativa against S. pneumonia (20.23 ± 0.16 mm), respectively. The concentrations of heavy metals were determined by atomic absorption spectrophotometer and showed the existence of high concentration of Iron (Fe), Lead (Pb) and Chromium (Cr). Thus, it has been found that medicinal plants individually as well as their recipes are potentially active against various diseases particularly respiratory tract infections.

Antibacterial Activities, Phytochemical Screening and Metal Analysis of Medicinal Plants: Traditional Recipes Used against Diarrhea.

The aim of this study was to explore the phytochemical composition, heavy metals analysis and the antibacterial activity of six medicinal plants i.e., Terminalia chebula Retz (fruits), Aegle marmelos L., (fruits), Curcuma longa L., (rhizomes), Syzygium aromaticum L., (flower buds), Piper nigrum L., (seeds), Cinnamomum cassia L., (barks) and its two remedial recipes (recipe 1 and 2) used against diarrhea obtained from the local herbal practitioners (Hakeems). A preliminary phytochemical screening of the above-mentioned plants extract in methanol, chloroform, n-hexane and distilled water revealed the presence of various constituents such as alkaloids, flavonoids, tannins and saponins by using standard procedures. The quantitative phytochemical studies shows that alkaloids, flavonoid and saponins were in maximum amount in Terminalia chebula. The concentration of Cd, Ni, Pb, Fe, Cr, Cu and Zn were investigated by using an atomic absorption spectrometer. The obtained analysis shows that Cr, Fe and Pb were present in the highest concentration in medicinal plants and their recipes. The antibacterial activities of the crude extract found in the recipes of methanol, chloroform, n-hexane and distilled water were analyzed by using agar well disc diffusion assay and minimum inhibitory concentration (MIC) by broth dilution method against four bacterial strains, namely, E. coli, Salmonella, Shigella and Methicillin-resistant Staphylococcus aureus (MRSA), respectively. The maximum zones of inhibition in methanol, water, chloroform and n-hexane extracts were seen in recipe 2 against Shigella (22.16 ± 0.47 mm), recipe 2 against Shigella (20.33 ± 0.24 mm), recipe 1 against Shigella (20.30 ± 0.29 mm) and recipe 2 against E. coli (30.23 ± 0.12 mm), respectively. Furthermore, the recipe extracts are more active against the tested bacterial strains than the extracts from individual plants. Therefore, it is concluded that the use of herbal plants and their recipes are the major source of drugs in a traditional medicinal system to cure different diseases.

Controllable synthesis of MoS2@MoO2 nanonetworks for enhanced NO2 room temperature sensing in air.

MoS2 nanosheets (NSs) are a promising gas sensing material at room temperature (RT) due to their unique properties and structures. Unfortunately, the activity of pure MoS2 NSs is highly affected by the adsorption of atmospheric oxygen, which strongly influences the stability of MoS2 sensing devices and significantly hinders the practical applications of these sensors in air. Heterostructure formation may be an effective approach to modulate the intrinsic electronic properties of MoS2 NSs. In this study, thin MoO2 nanoplates (NPs) were decorated with multilayer MoS2 NSs via one-step controllable sulfurization to fabricate MoS2@MoO2 nanonetworks, and remarkable gas sensing performance was achieved with high stability in air at RT. In particular, the MSO-2 (1 h sulfurization of the MoO2 NPs) nanonetworks with n-p heterojunctions demonstrated a high response of 19.4 to 100 ppm NO2 in a short period of time (1.06 s) with rapid recovery (22.9 s) to the baseline. The excellent gas sensing performance of the MSO-2 sensor is attributed to the synergistic effect of the MoS2 NSs and thin MoO2 NPs, which created heterojunctions/defects to easily transfer electrons and provide more active sites for NO2 gas. This simple synthetic method to design and fabricate n-p heterojunction sensors will be effective in commercial gas sensing applications.

One-step synthesis of palladium oxide-functionalized tin dioxide nanotubes: Characterization and high nitrogen dioxide gas sensing performance at room temperature.

Mesoporous palladium oxide (PdO)-functionalized tin dioxide (SnO2) composite nanotubes (SPCTs) were prepared via one-step synthesis by electrospinning technology using ethanol and N,N-dimethylformamide (DMF) as solvents. Compared with pure SnO2 nanotubes, there were abundant mesopores and multiheterojunctions in PdO-functionalized SnO2 nanotubes. The sample with the molar ratio of SnO2:PdO of 100:3 (3-SPCT) exhibited excellent response (∼20.30) as a sensor with fast gas response speed (∼1.33 s) to 100 ppm nitrogen dioxide (NO2) at room temperature (RT), and the detection limit reached to 10 ppb. The improved gas sensing performance of the 3-SPCT sensor was mainly attributed to the synergistic effect: the unique SnO2 tubular structure and well-dispersed mesopores provided the gas diffusion and adsorption channels, oxygen defects and chemisorbed oxygen were taken as the electron trap and charge transfer active sites, and a large number of heterojunctions acted as electron transport channels, thereby increasing the transfer rate.

Porous 3D flower-like CoAl-LDH nanocomposite with excellent performance for NO2 detection at room temperature.

The 3D flower-like CoAl-layered double hydroxide (CoAl-LDH) was successfully prepared using the functional template agent of fluoride ions via a facile one-step hydrothermal route. Various techniques proved that all the samples presented 3D flower-like microstructural morphology. Representatively, the CA-2 sample, which was synthesized with the molar ratio of Co : Al of 3.65 : 1, had considerably abundant pores in its thin nanosheets. The average pore size was 2-4 nm, the specific surface area was equal to 49.45 m2 g-1, and the thickness of nanosheets was approximately 3.068 nm. The CA-2 sample showed an excellent response to 0.01-100 ppm NO2 with ultrafast response/recovery time at room temperature (RT). The detection limit of the sensor even reached 10 ppb. The superior gas sensing performance could be attributed to the synergistic effects of the functional template agent of fluoride ions and specific porous 3D flower-like nanostructure. The current study showed that the 3D flower-like CoAl-LDHs might a promising material in practical detection of NO2 at RT.