Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical characteristics, predisposing factors, and treatment outcome of Curvularia keratitis

Purpose: To report clinical characteristics, predisposing factors, and treatment outcome of Curvularia keratitis. Methods: Retrospective chart review of consecutive culture-proven Curvularia keratitis patients who presented to a tertiary eye care center in north India. Patients with mixed infections with Curvularia as one of the pathogens were also included. Standard case report form was developed to capture demographic information, clinical features, etiology, treatment, and outcome. Binary logistic regression was done to ascertain the effect of identified variables on final visual acuity. Results: Medical records of 97 patients of Curvularia keratitis were reviewed. Median age of patients was 45.3 years. Seventy-nine (79.4%) patients presented during the months of September to November. History of corneal trauma was present in 69.1%. Trauma from sugarcane leaf was identified in 66.1% of cases with corneal trauma with vegetative matter. Presenting visual acuity was worse than 20/60 in 57.8% of patients. Hypopyon and pigmented plaque-like infiltrate was present in 16.5% and 28.8% of patients, respectively. Mixed infection was reported in 14.4% of cases. Median time of antifungal therapy was 24.5 days. Surgical intervention was required in 18.5% cases. Of all, 11.1% patients achieved final VA of more than 20/200 who were managed surgically as compared to 68 (86%) patients who were managed medically. Younger age, absence of comorbidities, and lesser infiltrate size were found associated with good final visual acuity. Conclusion: Working males were most affected by Curvularia keratitis. Corneal trauma with sugarcane leave was the most common predisposing factor in the study area. Most of the cases presented with worse visual acuity but could be managed medically.
Optimized Substrate for Improved Performance of Stacked Nanosheet Field-Effect Transistor

The recently proposed stacked nanosheet-field-effect transistor (SNSH-FET) is considered as a promising candidate for continued scaling with silicon. While using punchthrough-stopper-doped (or) ground-plane-doped silicon substrate (PTS-Si substrate) in which the top part of the substrate is doped heavily with the p-type (for nMOS) impurity to avoid punchthrough leakage between the source and the drain. The heavily doped p–n junction formed at the drain–substrate junction acts as a reverse-biased tunnel diode during ${V}_{{\text {DS}}}$ biasing, which leads to large substrate leakage current. We presented SuperSteep-Retrograde silicon substrate (SSR-Si substrate) configuration which reduces the tunneling current by increasing the tunnel barrier width and diminishing the peak electric field at the drain–substrate junction. The SSR-Si substrate is achieved by growing a lightly doped or undoped layer of silicon (SSR-buffer layer) on the PTS-doped substrate. The impact of SSR-buffer layer thickness is studied and the optimal thickness (12 nm) is presented. The vertically stacked channels’ configuration leads to position-dependent current densities in different channels due to position-dependent series resistance. Herein, we present nanosheet width optimization as a solution to achieve homogeneous current ratio between all the channels thereby resulting in better linearity performance. The self-heating and RF performance of the presented SSR-Si substrate is compared with the silicon-on-insulator (SOI) substrate. The results show that SSR-Si substrate can be a better substrate for SNSH-FET because of better self-heating performance.
Ecosystem Recovery in Amazon Caatinga Forest after Cutting, Cutting and Burning, and Bulldozer Clearing Treatments

A study of ecosystem recovery following forest cutting, forest cutting and burning, and forest clearing by bulldozing was conducted in Amazon caatinga forest in the upper Rio Negro Region of southern Venezuela. Ecosystem recovery was evaluated by measuring vegetation composition, biomass, nutrient accumulation, soil characteristics, and nutrient leaching. As disturbance increased in intensity, the early successional vegetation changed from primary forest trees (cut treatment) to successional woody species (cut and burned treatment) to forbs and grasses (bulldozed treatment). Soil nutrient levels were greater in both the cut and the cut and burned treatment plots than in the control forest three years after the disturbances presumably because of steady transfers of nutrients from the forest slash to the soil. Soil nutrient levels in the bulldozed plot were much lower than the control forest because of top soil removal. Aboveground biomass at three years was 1291 g m2 in the cut site, 870 g m2 in the cut and burned site, and 77 g m2 in the bulldozed site. Given these rates of biomass accumulation, approximately 100 yr will be required for both the cut and the cut and burned sites to reach biomass levels characteristic of mature caatinga forest, while more than 1000 yr will be necessary in the case of the bulldozed site.
Literature Review on Driver’s Drowsiness and Fatigue Detection

Traffic accidents always cause great material and human losses. One of the most important causes of these accidents is the human factor, which is usually caused by fatigue or drowsiness. To address this problem, several approaches were proposed to predict the driver state. Some solutions are based on the measurement of the driver behavior such as: the head movement, the duration of the blink of the eye, the observation of the mouth expression. … etc., while the others are based on the measurements of the physiological signals to get information about the internal state of the driver’s body. These measurements are collected using different sensors such as Electrocardiogram (ECG), Electromyography (EMG), Electroencephalography (EEG), and Electrooculogram (EOG). In this paper, we presented a literature review on the recent related works in this field. In addition, we compared the methods used in each measurement approach. Finally, a detailed discussion according to the methods efficiency as well as the achieved results will be given.
Computational Intelligence for Information Security: A Survey

Information security is the set of processes that protect information away from unauthorized access, disclosure, replication, modification, or destruction. Recently, more and more real-world systems such as smart cities, wireless sensor networks, biometric systems, and surveillance, require the assurances of information security. Thus, many different techniques based on computational intelligence have been developed for information security in the past decades. However, there are no comprehensive surveys that summarize these techniques. Thus, this paper reviewed computational intelligence approaches published in journals and conferences for information security in the last decade. This paper is a brief, but a comprehensive survey to review numerous computational intelligence approaches and applications for information security. A discussion of the existing challenges of computational intelligence approaches and techniques for information security is also presented.
Deep Radiomic Analysis to Predict Gleason Score in Prostate Cancer

Convolutional neural networks (CNNs) require large amounts of data for training, beyond what can be acquired for current radiomics models. We hypothesize that deep entropy features (DEFs) derived from existing CNNs can be applied to MRI images of prostate cancers (PCa) to reliably predict the Gleason score (GS) of PCa lesions. In this study, we analyzed 112 lesions acquired from 99 PCa patients, either pre-biopsy or pre-treatment, their associated GS, and multi-parametric MRI (mpMRI) sequences. Our approach is based on the extraction of DEF features produced in individual layers of 9 pre-trained CNN models. We first analyze DEFs from separate CNNs using the Wilcoxon test and Spearman correlation to find significant features associated with GS. In a multivariate analysis, we then use the combined DEFs of all CNNs as input to a random forest (RF) classifier for predicting the Gleason grade group of patients. Among the 9 pre-trained CNNs, the NASNet-mobile architecture offered the features most correlated to GS ( $\rho =0.47$ ; p < 0.05). From the 7,857 combined features, 11 DEFs could differentiate GS < 8 from GS ≥8 (corrected p < 0.05). Moreover, the RF classifier discerned GS of 6, 3+4, 4+3, 8 and ≥9 with an AUC (%) of 80.08, 85.77, 97.30, 98.20, and 86.51, respectively. Our results suggest that the DEFs can be used to differentiate GS of PCa lesions with the highest accuracy of GS ≥8 based on mpMRI. DEFs could improve diagnosis accuracy, reduce the risks of misclassification, help to better assess prognosis, and individualize patient care approaches.
Varietal differences in rumen degradation of barley, wheat and hard wheat straws

The rumen dry-matter degradation of straw from nine varieties of barley, nine varieties of wheat and seven varieties of hard wheat was studied to provide further information on varietal differences and their relationships with agronomic characteristics. In all the cereals there were significant differences in in situ dry-matter disappearance between varieties. The range of differences was lower in hard wheat varieties compared with the other cereals. There were no significant correlations between straw degradability and plant height, days from sowing to heading and grain production and quality. Lignin concentration was significantly correlated to straw degradability (r = −0·937 in barley, −0·675 in wheat and −0·987 in hard wheat). Leaf had lower concentrations of lignin and higher degradability than stem in barley and wheat but the two fractions were similar in hard wheat straws. The differences in degradation between the most and the least degradable straw varieties of each cereal species were mainly due to differences in degradability of both the fractions whereas the role of leaf and stem proportions was of minor importance.
Associations between Genotype–Diet Interactions and Weight Loss—A Systematic Review

Studies on the interactions between single nucleotide polymorphisms (SNPs) and macronutrient consumption on weight loss are rare and heterogeneous. This review aimed to conduct a systematic literature search to investigate genotype–diet interactions on weight loss. Four databases were searched with keywords on genetics, nutrition, and weight loss (PROSPERO: CRD42019139571). Articles in languages other than English and trials investigating special groups (e.g., pregnant women, people with severe diseases) were excluded. In total, 20,542 articles were identified, and, after removal of duplicates and further screening steps, 27 articles were included. Eligible articles were based on eight trials with 91 SNPs in 63 genetic loci. All articles examined the interaction between genotype and macronutrients (carbohydrates, fat, protein) on the extent of weight loss. However, in most cases, the interaction results were not significant and represented single findings that lack replication. The publications most frequently analyzed genotype–fat intake interaction on weight loss. Since the majority of interactions were not significant and not replicated, a final evaluation of the genotype–diet interactions on weight loss was not possible. In conclusion, no evidence was found that genotype–diet interaction is a main determinant of obesity treatment success, but this needs to be addressed in future studies.
Everolimus plus bevacizumab is an effective first‐line treatment for patients with advanced papillary variant renal cell carcinoma: Final results from a phase II trial

We previously reported on a phase 2 study of everolimus plus bevacizumab across various nonclear cell renal cell carcinoma (nccRCC) histologies and observed encouraging activity among patients with papillary RCC (pRCC) and unclassified RCC (uRCC) with a major papillary component. We subsequently expanded the study to enroll additional patients with pRCC variants.
Early Life Disruption of the Microbiota Affects Organ Development and Cytokine Gene Expression in Threespine Stickleback

Synopsis The microbiota that inhabits vertebrates exerts strong effects on host physiology and can be crucial to the development of a normal phenotype. This includes development of the immune system, somatic growth and maintenance, and morphogenesis. However, the genetic background of the host can also affect these life history traits. To this end, we investigated the effects of the microbiota on growth, development, and immune gene expression on two populations of threespine stickleback (Gasterosteus aculeatus), one anadromous and one freshwater. We tested the hypotheses that microbial colonization and the genetic background of the host would affect survival, cytokine gene expression, growth, and development. We raised in vitro crosses of stickleback larvae with and without conventional microbiota. We then exposed all these treatments to Vibrio anguillarum, a potential fish pathogen, in a full factorial design. We found stickleback raised without conventional microbiota had smaller swim bladders relative to those raised with conventional microbiota. Stickleback raised with conventional microbiota exhibited small increases in cytokine gene expression. We found no differences in growth or survival regardless of treatment. These results are consistent with other investigations that show microbiota disruption, in early life, can alter host organ and tissue development and immune responses
A Fully Integrated DAC for CMOS Position-Based Charge Qubits with Single-Electron Detector Loopback Testing

This letter presents a fully integrated interface circuitry with a position-based charge qubit structure implemented in 22-nm FDSOI CMOS. The quantum structure is controlled by a tiny capacitive DAC (CDAC) that occupies <inline-formula> <tex-math notation="LaTeX">$3.5\times 45\,\,\mu \text{m}^{2}$ </tex-math></inline-formula> and consumes 0.27mW running at a 2-GHz system clock. The state of the quantum structure is measured by a single-electron detector that consumes 1mW (including its output driver) with an area of <inline-formula> <tex-math notation="LaTeX">$40\times 25\,\,\mu \text{m}^{2}$ </tex-math></inline-formula>. The low power and miniaturized layout of these circuits pave the way for integration in a large quantum core with thousands of qubits, which is a necessity for practical quantum computers. The CDAC output noise of 12<inline-formula> <tex-math notation="LaTeX">$\mu \text{V}$ </tex-math></inline-formula>-rms is estimated through mathematical analysis while the ≤ 0.225mV-rms input referred noise of the detector is verified by measurements at 3.4 K. The functionality of the system and performance of the CDAC are verified in a loopback mode with the detector sensing the CDAC-induced electron tunneling from the floating diffusion node into the quantum structure.
MCFL: Improving Fault Localization by Differentiating Missing Code and Other Faults

Software testing is a popular practice to evaluate the software quality, and debugging is one of the most time-consuming tasks. In the last decades, spectrum-based fault localization (SBFL) techniques have been extensively studied and empirically shown effective in locating faults in a program. However, recent researches demonstrated that the accuracy of an SBFL technique may decrease when it is applied to a program containing code-omission faults. In this paper, we present a novel approach – MCFL. It models the behavior of code omission, embeds code-omission probes into programs to identify potential locations of missing code, captures spectra of program execution, and evaluates the suspiciousness of program entities being related to faults. Different from existing SBFL techniques, MCFL synthesizes a ranked list consisting of both suspicious statements and suspicious code-omission sites, which reflect the probability of a normal statement being faulty and the probability of missing code at specific positions in the program, respectively. We conducted a controlled experiment to compare the fault-localization accuracy of MCFL with those of four popular SBFL techniques. Six real-world projects from the dataset Defects4J are used as the experiment subjects. The experiment result showed that (i) MCFL outperforms the experimented SBFL techniques on most subjects, and on average has a 17.47% improvement; (ii) For more than 60% of the faults, MCFL successfully tells whether they are due to code omission.
Impact of a resistance gene against a fungal pathogen on the plant host residue microbiome: The case of the Leptosphaeria maculans–Brassica napus pathosystem

Abstract Oilseed rape residues are a crucial determinant of stem canker epidemiology as they support the sexual reproduction of the fungal pathogen Leptosphaeria maculans. The aim of this study was to characterize the impact of a resistance gene against L. maculans infection on residue microbial communities and to identify microorganisms interacting with this pathogen during residue degradation. We used near‐isogenic lines to obtain healthy and infected host plants. The microbiome associated with the two types of plant residues was characterized by metabarcoding. A combination of linear discriminant analysis and ecological network analysis was used to compare the microbial communities and to identify microorganisms interacting with L. maculans. Fungal community structure differed between the two lines at harvest, but not subsequently, suggesting that the presence/absence of the resistance gene influences the microbiome at the base of the stem whilst the plant is alive, but that this does not necessarily lead to differential colonization of the residues by fungi. Direct interactions with other members of the community involved many fungal and bacterial amplicon sequence variants (ASVs). L. maculans appeared to play a minor role in networks, whereas one ASV affiliated to Plenodomus biglobosus (synonym Leptosphaeria biglobosa) from the Leptosphaeria species complex may be considered a keystone taxon in the networks at harvest. This approach could be used to identify and promote microorganisms with beneficial effects against residue‐borne pathogens and, more broadly, to decipher the complex interactions between multispecies pathosystems and other microbial components in crop residues.
Cluster of cases of congenital feline goitrous hypothyroidism in a single hospital.

OBJECTIVES To describe the clinicopathological findings and outcomes of cases of feline congenital hypothyroidism diagnosed in a single veterinary hospital in Santiago, Chile. MATERIALS AND METHODS Medical records were searched for cases of congenital hypothyroidism over an 18-month period. Inclusion criteria were a diagnosis of congenital hypothyroidism based on consistent historical and clinical findings, a low or low-normal serum total T4 and elevated serum canine TSH (cTSH). RESULTS Six unrelated cats ranging in age from 4 to 19 months met the inclusion criteria. The most common historical signs were small stature and lethargy. All cats had disproportionate dwarfism, delayed tooth eruption, retained deciduous teeth, bilateral palpable goitres and low rectal temperatures. Other findings were bradycardia, obesity, poor hair coat and focal alopecia on the ventral aspects of the elbows and hocks. In all cases, cTSH was markedly elevated. Sequential changes noted after the initiation of therapy included normal T4 after 6 weeks, improved hair coat and increased physical activity by 8 weeks, normal cTSH by 10 weeks and normal physical appearance and dentition after 4 months. Goitres shrank markedly but remained palpable. Hypothyroidism was well managed clinically in all cases 2 years after diagnosis except for one cat that died of unrelated causes. CLINICAL SIGNIFICANCE This is the first report to describe a cluster of congenital hypothyroidism cases in non-related cats that were presented over a short period of time. Growth defects resolve with treatment, even in cats diagnosed after puberty. Larger, prospective multi-centre studies are warranted to determine the incidence of congenital hypothyroidism in cats.
A Step Closer to Becoming Symbiotic with AI through EEG: A Review of Recent BCI Technology

Our brain produces electrical signals when neurons communicate with each other. The process of extracting that information of rich electrical activity through noninvasive electrodes is called Electroencephalography (EEG). With the recent surge and advancement in machine learning technology and computational power, the information is further processed to extract patterns in order to derive knowledge. The synthesized knowledge can be used in any desired fashion, ranging from executing commands to analyzing patterns. In recent times, EEG has been a compelling candidate to directly interact with different interfaces just through our brain waves, without depending upon muscular movements and peripheral nerves. It also extends its applicability in various fields, such as psychology, robotics, and cybersecurity, and aiding in the treatment and rehabilitation of various neurological disorders. In this paper, we review what has been out by the end of the 2nd decade of the 21st century. It helps readers, novice to experts, understand two areas:(1) Latest developments in the brain-computer interface (BCI) field, and (2) Different devices and their properties used to execute those developments. The aim of this paper is to help promote advancement and further development in this area.
Eye-tracking Based Performance Analysis in Error Finding Programming Test

Error finding is a widely-used kind of test to assess the students’ ability in program comprehension and debugging. Recently some eye-tracking based approaches have been proposed to analyze students’ cognitive process in programming, but few of them focused on the performance assessment in programming courses. In our previous work, an eye-tracking assisted framework was proposed to assess the overall performance of one student in a web-based error-find test. Aiming to provide individual diagnostics, we extend that work in this paper, and study 15 students’ performance in different phases of the online test. For each student, his performance is analyzed in two phases, i.e. the code overview phase and the subsequent phase. In the first phase, the behavior of first-time code browsing and first-time error finding is studied and compared. We find that the performance of first error-finding action has obvious correlation with the final test score. In the subsequent phase, the students’ performances are examined in activity sequence separated by the click events. We propose a new metric, namely matched ratio of program execution, to describe the proportion of gaze path matching with the program execution. Data analysis results show that, it is more helpful in interpreting students’ behavior and describing the students’ working efficiency in the test.
First report of Banksinoma exobothridialis (Acari: Oribatida: Thyrisomidae) from Iran

The faunistic survey of oribatid mites, between 2008 and 2010, on the outskirts of the city of Shiraz led to the discovery of the species Banksinoma exobothridialis Bayartogtokh of the family Thyrisomidae. Both the species and the family are new to the Iranian fauna. �� � �ƱŶŝ�ƩƺƏ Nac � eic � �ƹ �Ʊō�ƉźƗ cei � ccO � Ư ǀ Ź�ƶŝ�ƱŶŝ�źŤƯƹźĪ �ŵŹŻ�Śţ�ŵŹŻ�ĬƳ ŚƯ ƿ �ƺoƸƣ�ƶŝ�ƪ � ř ƽ �ŨƬŨƯ�ƭƺoƏźų� ƾ � �ƺoƳ�ŚoƸŤƳř�Źŵ�ƹ � ţ �ǀ z � ŚƷƺƯ ƽ � ƯƺƏźų ƾ � Ʈĩ � �ŝƹ ǀ �Źřŵ�ƹ�ŶoƴƬŝ�Ƃ ƽ � ƺƯ ƿ �oĤ � �oƷ ƽ