Miracle-dz/UniFAR-FTUs huggingface dataset:
Sarcoid Uveitis: An Experience from a Tertiary Care Hospital in Pakistan.

Objectives: The Primary objective was to find the frequency of Sarcoid Uveitis among the patients pre- sented with Uveitis.The secondary objective was to follow the patients of sarcoid uveitis over treat- ment. Methods: We did an observational prospective study, after acquiring ethical review letter from IRB Medicine department outpatient services of Jinnah Medical College Hospital Korangi was used where patients with Uveitis were referred for evaluation of systemic disease during jan 2019 to dec 2020. These patients underwent history, examination and biochemical and radiological investigations and were followed over a period of 1 year to see response to treatment. Results: A total of 372 patients presented with established uveitis out of which 36 patients i.e. 66 eyes presented to our eye clinic with decrease in vision with a mean age of 42±2 years. Among 36 patients, 89% presented with redness of eye and 72% presented with eye pain where as 8 % pre- sented with photophobia and floaters. Biochemically >10mg/dl calcium was observed in 18(50%) pa- tients and ACE level was found to be more than 40nmol/ml/min in 30(83.3%) patients. Among them 32 (88.9%) patients showed resolved inflammation whereas 2(5.56%) showed a change in inflamma- tion and 2(5.56%) showed no change. Conclusion: We found sarcoid uveitis as fairly common cause of uveitis. The patients were treated with the standard treatment of steroids and immunosuppressants i.e. Azathioprine and we concluded that use of steroids and immunosuppressant improved the outcome in our patients. The patients re- sponded fairly well to the designed treatment.
Allergologia et immunopathologia

Background : The present study aimed to evaluate the quality of life, depression, and anxiety scores of children with primary immunodeficiency (PID) and depression, anxiety scores, and the caregiving burden of their mothers. Methods : A total of 149 children aged 2-18 years and their mothers were included in the pres-ent study, along with 125 healthy children and their mothers as a control group. The Pediatric Quality of Life Inventory (PedsQL), Child Depression Inventory (CDI), and Screening for Child Anxiety-Related Emotional Disorders (SCARED) questionnaire were used based on the views of children and their mothers. The Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Temperament Evaluation of Memphis, Pisa, Paris, San Diego Autoquestionnaire (TEMPS-A), and Zarit Caregiver Burden Scale (ZCB) were used for the mothers. Results : According to children and their mothers, the scores of the PedsQL were lower than that of the control group (P < 0.05). In addition, according to the views of children and moth - ers, we found that PID children had higher depression and anxiety scores than healthy children (P < 0.05). The depression and anxiety levels of mothers in the patient group were also significantly higher than those in the control group (P = 0.05 and P = 0.001). Conclusion : Statistically, we found significantly lower psychosocial health summary scores and total scale score levels from the subclass of PedsQL in the patient group than in the control group. According to the views of both children and mothers, we observed that PID children had higher depression and anxiety scores than healthy children. It was also found that the BDI and BAI values in case of mothers in the patient group were significantly higher than those in the control group.
The assassin bug subfamilies Centrocnemidinae and Holoptilinae in Taiwan (Hemiptera: Heteroptera: Reduviidae)

The assassin bugs (Hemiptera: Heteroptera: Reduviidae) of the sub- family Centrocnemidinae and Holoptilinae of Taiwan are surveyed, the taxa are diagnosed, illustrated, and keyed. The nomenclature of the family group name Cen- trocnemidinae is discussed. Of this subfamily, the single species Neocentrocnemis stali (Reuter, 1881) is recognized, old records of Centrocnemis deyrollii Signoret, 1852 are considered as misidentifi cations of N. stali. The following synonymies are proposed: Neocentrocnemis stali (Reuter, 1881) = N. formosana (Matsumura, 1913), syn. nov., = N. baudoni Dispons, 1965, syn. nov. The male and female genitalia of N. stali are illustrated in detail, the morphology and homologies of the male intromittent organ are discussed. Of Holoptilinae, two genera and three species are recognized. The genus Locoptiris Villiers, 1943, described from the Afrotropical Region, is recorded for the fi rst time from Taiwan; this record means also the fi rst record of the genus in the Oriental Region. Locoptiris taiwanensis sp. nov. is described as new. Ptilocerus pendleburyi Miller, 1940, described from peninsular Malaysia, is transferred to Locoptiris. Species of Locoptiris are keyed, the relationships within the genus are discussed. Ptilocerus immitis Uhler, 1896, so far known only from Japan, is recorded for the fi rst time from Taiwan.
Evaluation of accompanying allergic disease in children with Evaluation of accompanying allergic disease in children with proven drug allergies proven drug allergies

, producing a positive result in 60% (n = 31). The prevalence of allergic disease was not differing according to the culprit drug. The frequency of developing at least one concomitant allergic disease was 47.2% (n = 50/106) for antibiotic hypersensitivity, 52.4% (n = 22/42) for NSAID hypersensitivity, and 15% (n = 3/20) for anticonvulsant hypersensitivity (p < 0.00). Immediate drug hypersensitivity reactions were more frequent in children who had allergic diseases (80 % vs. 64.5 %; p = 0.027). Conclusion: Nearly half (44.6%) of the children with proven drug hypersensitivity had concomitant allergic diseases and immediate reactions were more common in this group. Children evaluated for drug hypersensitivity should be assessed for other allergic diseases.
Creating Knowledge of the Competitiveness of Enterprises in the Context of Changes in Management Processes

: The unpredictable conditions of the competitive environment of modern enterprises force managers to look for new ways of managing enterprises. Furthermore, the diversity of the competitive environment requires the introduction of changes in the management processes of the company, which will enable building a competitive advantage and will allow for its sustainability. Therefore, the study raises the research question: how to create knowledge of the competitiveness of enterprises in the context of changes in management processes. The study presents the concept according to which the introduction of changes in management processes requires the creation of knowledge, which will enable distinguishing the company against its competitors. Therefore, the objective of the study has been the in-depth understanding of the creation of knowledge of the competitiveness of enterprises in the context of changes that can be introduced in management processes. Moreover, the publication deepens the understanding of the relationship between competitiveness factors determining the company’s capabilities, its prospects, and the projection of changes in the environment. The research methods applied to accomplish the assumed objective are critical analysis of literature, a survey questionnaire carried out in 2022 and Pearson’s correlation analysis. The empirical research was conducted on the basis of data obtained from one hundred enterprises from Poland. The conclusions from the research suggest that managers’ attention ought to be focused on identifying the factors of competitiveness of enterprises that enable them to achieve a competitive advantage.
Forensic Linguistic Analysis: Civil Servant Speech Equating Prostitutes with Probolinggo Parliament Members

The research investigated an alleged defamation statement made by a civil servant in Probolinggo, suggesting that the Regional People's Representative Council of Probolinggo (DPRD Probolinggo) held a lower social status than local sex workers, employing forensic linguistics as its analytical framework. The research was a single case study that applied a qualitative approach to analyze the data. A single case study was a suitable method for analyzing a single case of an individual; here was a civil servant in a socialization event attended by the regional government. The data consisted of four sentences of verbal statements and eight accompanying non-verbal cues made by the civil servant toward Probolinggo Parliament members. The research uncovers that the civil servant's remarks contain pointed appraisals and judgments aimed at DPRD Probolinggo members, ultimately leading to legal action predicated on allegations of defaming the collective dignity of DPRD Probolinggo. However, it is important to emphasize that these critical assessments are specific to certain members rather than constituting a comprehensive censure of the entire institutional body. Consequently, the civil servant may potentially be exempt from institutional defamation charges, with these actions interpreted as robust workplace criticisms. Nevertheless, the research underscores the potential legal repercussions stemming from such critiques and the need for a meticulous evaluation of their legal implications within the legal framework, given their potential to generate future legal disputes.
MIT Open Access Articles Dissociation of Detection and Discrimination of Pure Tones following Bilateral Lesions of Auditory Cortex

It is well known that damage to the peripheral auditory system causes deficits in tone detection as well as pitch and loudness perception across a wide range of frequencies. However, the extent to which to which the auditory cortex plays a critical role in these basic aspects of spectral processing, especially with regard to speech, music, and environmental sound perception, remains unclear. Recent experiments indicate that primary auditory cortex is necessary for the normally-high perceptual acuity exhibited by humans in pure-tone frequency discrimination. The present study assessed whether the auditory cortex plays a similar role in the intensity domain and contrasted its contribution to sensory versus discriminative aspects of intensity processing. We measured intensity thresholds for pure-tone detection and pure-tone loudness discrimination in a population of healthy adults and a middle-aged man with complete or near-complete lesions of the auditory cortex bilaterally. Detection thresholds in his left and right ears were 16 and 7 dB HL, respectively, within clinically-defined normal limits. In contrast, the intensity threshold for monaural loudness discrimination at 1 kHz was 6.5 6 2.1 dB in the left ear and 6.5 6 1.9 dB in the right ear at 40 dB sensation level, well above the means of the control population (left ear: 1.6 6 0.22 dB; right ear: 1.7 6 0.19 dB). The results indicate that auditory cortex lowers just-noticeable differences for loudness discrimination by approximately 5 dB but is not necessary for tone detection in quiet. Previous human and Old-world monkey experiments employing lesion-effect, neurophysiology, and neuroimaging methods to investigate the role of auditory cortex in intensity processing are reviewed.
Authors’ response to Reviewer 1’s comments for ”Bridging classical data assimilation and optimal transport”

The paper discusses a very interesting and pertinent problem and proposes a solution that is certainly worth looking at further. The continued use of quadratic error functional is somewhat anachronistic given that the main motivation for using it are simplicity of analytic computations and an assumption of Gaussian errors. The first argument carries less weight in the age of super-computers
When debugging encounters artiﬁcial intelligence: state of the art and open challenges

Both software debugging and artiﬁcial intelligence techniques are hot topics in the current ﬁeld of software engineering. Debugging techniques, which comprise fault localization and program repair, are an important part of the software development lifecycle for ensuring the quality of software systems. As the scale and complexity of software systems grow, developers intend to improve the eﬀectiveness and eﬃciency of software debugging via artiﬁcial intelligence (artiﬁcial intelligence for software debugging, AI4SD). On the other hand, many artiﬁcial intelligence models are being integrated into safety-critical areas such as autonomous driving, image recognition, and audio processing, where software debugging is highly necessary and urgent (software debugging for artiﬁcial intelligence, SD4AI). An AI-enhanced debugging technique could assist in debugging AI systems more eﬀectively, and a more robust and reliable AI approach could further guarantee and support debugging techniques. Therefore, it is important to take AI4SD and SD4AI into consideration comprehensively. In this paper, we want to show readers the path, the trend, and the potential that these two directions interact with each other. We select and review a total of 165 papers in AI4SD and SD4AI for answering three research questions, and further analyze opportunities and challenges as well as suggest future directions of this cross-cutting area.
Testing Scenario Identiﬁcation for Automated Vehicles Based on Deep Unsupervised Learning

: Naturalistic driving data (NDD) are valuable for testing autonomous driving systems under various driving conditions. Automatically identifying scenes from high-dimensional and unlabeled NDD remains a challenging task. This paper presents a novel approach for automatically identifying test scenarios for autonomous driving through deep unsupervised learning. Firstly, US DAS2 NDD are leveraged, and the selection of data variables representing the vehicle state and surrounding environment is conducted to formulate the segmentation criterion. The isolation forest (IF) algorithm is then employed to segment the data, yielding two distinct types of datasets: typical scenarios and extreme scenarios. Secondly, a one-dimensional residual convolutional autoencoder (1D-RCAE) is developed to extract scenario features from the two datasets. Compared to four other autoencoders, the 1D-RCAE can effectively extract crucial information from high-dimensional data with optimal feature extraction capability. Next, considering the varying importance of different features, an information entropy (IE)-optimized K-means algorithm is employed to cluster the features extracted using 1D-RCAE. Finally, statistical analysis is performed on the parameters of each cluster of scenarios to explore their distribution characteristics within each class, and four typical scenarios are identiﬁed along with ﬁve extreme scenarios. The proposed unsupervised framework, combining IF, 1D-RCAE, and IE-improved K-means algorithms, can automatically identify typical and extreme scenarios from NDD. These identiﬁed scenarios can then be applied to test the performance of autonomous driving systems, enriching the library of automated driving test scenarios.
Radiosonde humidity corrections and potential Atmospheric Infrared Sounder moisture accuracy

[1] Although there are a number of sources of radiosonde data for validation of observations from other atmospheric sensors, routine operational sondes remain the main source for a large volume of data. In this study radiosonde moisture profiles are renormalized using Global Positioning System (GPS) Integrated Precipitable Water (IPW) vapor. The GPS-adjusted radiosonde humidity profiles are then compared to the Atmospheric Infrared Sounder (AIRS) measurements. As a check, AIRS measurements are also compared with unadjusted radiosonde moisture profiles. It is shown that the GPS-adjusted values are in better agreement with the AIRS measurements. On the basis of this result, the GPS-adjusted radiosondes are used to assess the AIRS potential accuracy. This is valid because the errors in the AIRS measurements and the adjustments are independent. The GPS-based renormalization of radiosonde humidity measurements produced a significant improvement in the agreement between AIRS and Vaisala RS 57 H type radiosondes in the lower troposphere, where much of the atmospheric water vapor resides. The adjustment also resulted in improved agreement between AIRS and radiosonde IPW estimates. The results showed a day/night bias in the radiosonde values as compared to the GPS and the AIRS values, demonstrating the potential use of the technique for evaluating and correcting this bias. Established corrections for humidity errors also have been applied to some operational radiosonde observations, specifically the published temperature correction developed for the Vaisala RS80 H type radiosonde. This correction produced a much smaller effect than the GPS adjustment.
Assessment of Soil Quality of Smallholder Agroecosystems in the Semiarid Region of Northeastern Brazil

The assessment of soil quality is crucial for the sustainable development of agriculture in semiarid regions. Due to their sensitivity to management practices, soil chemical and physical quality indicators are used for investigating soil quality. This study aimed to assess the soil quality of smallholder agroecosystems from the Brazilian semiarid region. Soil physical and chemical attributes were screened using principal component analysis (PCA) and integrated into a weighted additive soil quality index (SQI). Soil quality was obtained using linear and non-linear scoring methods, a total data set (TDS), and a minimum data set (MDS). The soil quality of the agroecosystems was designated as being of moderate grade. The MDS for soil quality assessment includes cation exchange capacity, C stock, exchangeable sodium percentage, flocculation degree, pH, electrical conductivity, available P, and K+ from twenty-five indicators of the TDS. This MDS mainly reflects the input of manure and crop residues associated with moderate weathering of easily weatherable minerals given the semiarid conditions. The SQI obtained can be used to synthesize the information of the TDS and is a valuable tool to indicate the soil quality of agroecosystems; thereby, it can be used with indicators of sustainable management for application at a regional scale.
Using computer vision to detect and segment fire behavior 1 classifications in UAS-captured images

16 The widely adaptable capabilities of artificial intelligence, in particular deep learning and computer 17 vision has led to significant research output regarding fire and smoke detection. Previous studies 18 often focus on themes like early fire detection, increased operational awareness, and post-fire 19 assessment. To further test the capabilities of deep learning detection in these scenarios, we collected 20 and labeled a unique aerial image dataset that determined whether specific types of fire behavior 21 could be reliably detected in prescribed fire settings. Our 960 labeled images were sourced from over 22 20.97 hours of UAS video collected during prescribed fire operations covering a large region of 23 Texas and Louisiana, U.S. National Wildfire Coordinating Group (NWCG) fire behavior 24 observations and descriptions served as a reference for determining fire behavior classes during 25 labeling. YOLOv8 models were trained on NWCG Rank 1-3 fire behavior descriptions in grassland, 26 shrubland, forested, and combined fire regimes within our study area. Models were first trained and 27 validated on isolated image objects of fire behavior, and then on segmenting fire behavior in their 28 original parent images. Models trained using isolated image objects of fire behavior consistently 29 performed at a mAP of 0.808 or higher, with combined fire regimes producing the best results (mAP 30 = 0.897). Most segmentation models performed relatively poorly, except for the forest regime model 31 at a box and mask mAP of 0.59 and 0.611, respectively. Our results indicate that classifying fire 32 behavior with computer vision is possible in most fire regimes and fuel models, whereas segmenting 33 fire behavior around background information is relatively difficult. However, it may be a manageable 34 task with enough data, and when models are developed for a specific fire regime. With an increasing 35 number of destructive wildfires and new challenges confronting fire managers, identifying how new 36 technologies can quickly assess wildfire situations can assist wildfire responder awareness. Our 37 conclusion is that levels of abstraction deeper than mere detection of smoke or fire are possible using 38 computer vision, and could make even more detailed fire monitoring possible.
Neurobiology of Disease

Neuronal networks can synchronize their activity through excitatory and inhibitory connections, which is conducive to synaptic plasticity. This synchronization is re ﬂ ected in rhythmic ﬂ uctuations of the extracellular ﬁ eld. In the hippocampus, theta and gamma band LFP oscillations are a hallmark of the processing of spatial information and memory. Fragile X syndrome (FXS) is an intellectual disability and the most common genetic cause of autism spectrum disorder (Belmonte and Bourgeron, 2006). Here, we investigated how neuronal network synchronization in the mouse hippocampus is compromised by the Fmr1 mutation that causes FXS (Santos et al., 2014), relating recently observed single-cell level impairments (Arbab et al., 2017) to neuronal network aberrations. We implanted tetrodes in hippocampus of freely moving Fmr1 -KO and littermate wildtype (WT) mice (Mientjes et al., 2006), to record spike trains from multiple, isolated neurons as well as LFPs in a spatial exploration paradigm. Compared to wild type mice, Fmr1 -KO mice displayed greater power of hippocampal theta oscillations, and higher coherence in the slow gamma band. Additionally, spike trains of Fmr1 -KO interneurons show decreased spike-count correlations and they are hypersynchronized with theta and slow gamma oscillations. The hy-persynchronization of Fmr1 -KO oscillations and spike timing re ﬂ ects functional de ﬁ cits in local networks. This network hypersynchronization pathologically decreases the heterogeneity of spike-LFP phase coupling, compromising information processing within the hippocampal circuit. These ﬁ ndings may re ﬂ ect a pathophysiological mechanism explaining cognitive impairments in FXS and autism, in which there is anomalous processing of social and environmental cues and associated de ﬁ cits in memory and cognition.
Stingless bees honeys': physical-chemical characterization, difficulties and challenges

Stingless bees form a group of eusocial bees belonging to the tribe Meliponini (Hymenoptera: Apidae) and are distributed in tropical a nd subtropical regions of the planet. Brazil, the main territory regarding stingless bees’ diversity, has a large variety of phytogeographic regions that can influence the physical-chemical properties of the honey. Currently, the country does not have a legislation that represents its territorial reality as a whole, making the parameters unattainable for producers to market s tingless bees’ honey. In addition to its economic importance for producers, this product has sensory characteristics that are different from those produced by Apis mellifera , making it a product with high added value. Additionally, honey production is a sustainable practice and increases bee conservation, which, in general, are mainly threatened by human interference. Thus, the objective of this work was to analyze the scientific productions published in consolidated databases to understand how bees deal with different environmental and geographic conditions and how this reflects on honey characteristics, in addition, to generate data to prepare a proposal to standardize and market the honey. For this, the StART software was used, with the elaboration of a systematic review protocol. A total of 93 articles were listed in the initial search in the Web of Science, Scopus and Google Scholar databases and after the selection and execution process, 50 articles were selected. In the summarization phase, it was observed that there is still no consensus among researchers on how to stipulate a standard and reference ranges for the analyses.